Upload folder using huggingface_hub

added_tokens.json

@@ -0,0 +1,7 @@
+{
+  "</s>": 2,
+  "<s>": 1,
+  "<unk>": 0,
+  "<|im_end|>": 32000,
+  "<|im_start|>": 32001
+}

config.json

@@ -0,0 +1,25 @@
+{
+  "_name_or_path": "mistralai/Mistral-7B-v0.1",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 32000,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.34.0.dev0",
+  "use_cache": true,
+  "vocab_size": 32002
+}

configs/samantha-mistral-7b.yml

@@ -0,0 +1,70 @@
+base_model: mistralai/Mistral-7B-v0.1
+base_model_config: mistralai/Mistral-7B-v0.1
+model_type: MistralForCausalLM
+tokenizer_type: LlamaTokenizer
+is_mistral_derived_model: true
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+datasets:
+  - path: ehartford/samantha-data
+    data_files: samantha-1.1.json
+    type: sharegpt
+    conversation: chatml
+    
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.01
+output_dir: /workspace/samantha-1.2-mistral-7b
+
+sequence_len: 8192
+sample_packing: true
+pad_to_sequence_len: true
+
+wandb_project: samantha
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+
+gradient_accumulation_steps: 4
+micro_batch_size: 6
+num_epochs: 40
+adam_beta2: 0.95
+adam_epsilon: 0.00001
+max_grad_norm: 1.0
+lr_scheduler: cosine
+learning_rate: 0.000006
+
+train_on_inputs: false
+group_by_length: false
+bf16: true
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+eval_steps: 0.05
+eval_table_size:
+eval_table_max_new_tokens:
+save_steps:
+debug:
+deepspeed: deepspeed/zero2.json
+weight_decay: 0.1
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "<|im_end|>"
+  unk_token: "<unk>"
+tokens:
+  - "<|im_start|>"
+  - "<|im_end|>"

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.35.0.dev0"
+}

latest

@@ -0,0 +1 @@
+global_step187

pytorch_model-00001-of-00002.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:68273d733eb811024f8dc9b3b480f91a42a5ca44f6f09bbd7843870144ac91fe
+size 9943044428

pytorch_model-00002-of-00002.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:43cae66c14fad9e246e477d92ab15c9d9b76c2d48dd9c36b2f01c5c5eacd4df7
+size 4540552031

pytorch_model.bin.index.json

@@ -0,0 +1,298 @@
+{
+  "metadata": {
+    "total_size": 14483496960
+  },
+  "weight_map": {
+    "lm_head.weight": "pytorch_model-00002-of-00002.bin",
+    "model.embed_tokens.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.0.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.0.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.0.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.0.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.0.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.0.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.0.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.0.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.0.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.1.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.1.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.1.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.1.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.1.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.1.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.1.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.1.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.1.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.10.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.10.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.10.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.10.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.10.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.10.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.10.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.10.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.10.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.11.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.11.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.11.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.11.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.11.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.11.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.11.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.11.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.11.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.12.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.12.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.12.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.12.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.12.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.12.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.12.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.12.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.12.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.13.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.13.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.13.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.13.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.13.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.13.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.13.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.13.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.13.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.14.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.14.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.14.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.14.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.14.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.14.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.14.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.14.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.14.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.15.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.15.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.15.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.15.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.15.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.15.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.15.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.15.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.15.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.16.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.16.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.16.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.16.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.16.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.16.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.16.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.16.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.16.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.17.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.17.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.17.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.17.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.17.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.17.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.17.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.17.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.17.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.18.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.18.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.18.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.18.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.18.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.18.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.18.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.18.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.18.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.19.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.19.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.19.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.19.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.19.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.19.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.19.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.19.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.19.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.2.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.2.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.2.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.2.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.2.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.2.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.2.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.2.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.2.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.20.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.20.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.20.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.20.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.20.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.20.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.20.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.20.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.20.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.21.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.21.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.21.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.21.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.21.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.21.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.21.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.21.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.21.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.22.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.22.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.22.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.22.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.22.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.22.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.22.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.22.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.22.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.23.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.23.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.23.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.23.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.23.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.23.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.23.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.23.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.23.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.24.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.24.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.24.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.24.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.24.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.24.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.24.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.24.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.24.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.25.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.25.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.25.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.25.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.25.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.25.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.25.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.25.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.25.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.26.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.26.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.26.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.26.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.26.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.26.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.26.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.26.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.26.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.27.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.27.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.27.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.27.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.27.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.27.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.27.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.27.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.27.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.28.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.28.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.28.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.28.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.28.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.28.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.28.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.28.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.28.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.29.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.29.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.29.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.29.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.29.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.29.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.29.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.29.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.29.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.3.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.3.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.3.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.3.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.3.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.3.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.3.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.3.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.3.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.30.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.30.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.30.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.30.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.30.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.30.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.30.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.30.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.30.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.31.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.31.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.31.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.31.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.31.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.31.self_attn.k_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.31.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.31.self_attn.q_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.31.self_attn.v_proj.weight": "pytorch_model-00002-of-00002.bin",
+    "model.layers.4.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.4.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.4.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.4.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.4.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.4.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.4.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.4.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.4.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.5.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.5.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.5.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.5.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.5.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.5.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.5.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.5.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.5.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.6.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.6.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.6.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.6.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.6.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.6.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.6.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.6.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.6.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.7.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.7.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.7.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.7.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.7.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.7.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.7.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.7.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.7.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.8.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.8.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.8.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.8.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.8.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.8.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.8.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.8.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.8.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.norm.weight": "pytorch_model-00002-of-00002.bin"
+  }
+}

special_tokens_map.json

@@ -0,0 +1,6 @@
+{
+  "bos_token": "<s>",
+  "eos_token": "<|im_end|>",
+  "pad_token": "</s>",
+  "unk_token": "<unk>"
+}

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dadfd56d766715c61d2ef780a525ab43b8e6da4de6865bda3d95fdef5e134055
+size 493443

tokenizer_config.json

@@ -0,0 +1,61 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32000": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "32001": {
+      "content": "<|im_start|>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": true,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "trust_remote_code": false,
+  "unk_token": "<unk>",
+  "use_default_system_prompt": true,
+  "use_fast": true
+}

trainer_state.json

@@ -0,0 +1,1293 @@
+{
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 34.0,
+  "eval_steps": 10,
+  "global_step": 187,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "epoch": 0.18,
+      "learning_rate": 0.0,
+      "loss": 2.0388,
+      "step": 1
+    },
+    {
+      "epoch": 0.18,
+      "eval_loss": 14.199334144592285,
+      "eval_runtime": 1.4513,
+      "eval_samples_per_second": 45.477,
+      "eval_steps_per_second": 2.067,
+      "step": 1
+    },
+    {
+      "epoch": 0.36,
+      "learning_rate": 6.000000000000001e-08,
+      "loss": 11.2905,
+      "step": 2
+    },
+    {
+      "epoch": 0.55,
+      "learning_rate": 1.2000000000000002e-07,
+      "loss": 11.3574,
+      "step": 3
+    },
+    {
+      "epoch": 0.73,
+      "learning_rate": 1.8e-07,
+      "loss": 10.3132,
+      "step": 4
+    },
+    {
+      "epoch": 0.91,
+      "learning_rate": 2.4000000000000003e-07,
+      "loss": 8.2949,
+      "step": 5
+    },
+    {
+      "epoch": 1.09,
+      "learning_rate": 3.0000000000000004e-07,
+      "loss": 5.3189,
+      "step": 6
+    },
+    {
+      "epoch": 1.27,
+      "learning_rate": 3.6e-07,
+      "loss": 2.2121,
+      "step": 7
+    },
+    {
+      "epoch": 1.45,
+      "learning_rate": 4.2000000000000006e-07,
+      "loss": 2.0155,
+      "step": 8
+    },
+    {
+      "epoch": 1.64,
+      "learning_rate": 4.800000000000001e-07,
+      "loss": 1.9936,
+      "step": 9
+    },
+    {
+      "epoch": 1.82,
+      "learning_rate": 5.4e-07,
+      "loss": 1.8803,
+      "step": 10
+    },
+    {
+      "epoch": 1.82,
+      "eval_loss": 2.1710054874420166,
+      "eval_runtime": 1.463,
+      "eval_samples_per_second": 45.113,
+      "eval_steps_per_second": 2.051,
+      "step": 10
+    },
+    {
+      "epoch": 2.0,
+      "learning_rate": 6.000000000000001e-07,
+      "loss": 1.8794,
+      "step": 11
+    },
+    {
+      "epoch": 2.18,
+      "learning_rate": 6.6e-07,
+      "loss": 1.884,
+      "step": 12
+    },
+    {
+      "epoch": 2.36,
+      "learning_rate": 7.2e-07,
+      "loss": 1.8201,
+      "step": 13
+    },
+    {
+      "epoch": 2.55,
+      "learning_rate": 7.8e-07,
+      "loss": 1.8356,
+      "step": 14
+    },
+    {
+      "epoch": 2.73,
+      "learning_rate": 8.400000000000001e-07,
+      "loss": 1.7696,
+      "step": 15
+    },
+    {
+      "epoch": 2.91,
+      "learning_rate": 9e-07,
+      "loss": 1.6433,
+      "step": 16
+    },
+    {
+      "epoch": 3.09,
+      "learning_rate": 9.600000000000001e-07,
+      "loss": 1.5564,
+      "step": 17
+    },
+    {
+      "epoch": 3.27,
+      "learning_rate": 1.0200000000000002e-06,
+      "loss": 1.4976,
+      "step": 18
+    },
+    {
+      "epoch": 3.45,
+      "learning_rate": 1.08e-06,
+      "loss": 1.4484,
+      "step": 19
+    },
+    {
+      "epoch": 3.64,
+      "learning_rate": 1.14e-06,
+      "loss": 1.4277,
+      "step": 20
+    },
+    {
+      "epoch": 3.64,
+      "eval_loss": 1.5980714559555054,
+      "eval_runtime": 1.4588,
+      "eval_samples_per_second": 45.243,
+      "eval_steps_per_second": 2.056,
+      "step": 20
+    },
+    {
+      "epoch": 3.82,
+      "learning_rate": 1.2000000000000002e-06,
+      "loss": 1.4004,
+      "step": 21
+    },
+    {
+      "epoch": 4.0,
+      "learning_rate": 1.26e-06,
+      "loss": 1.3963,
+      "step": 22
+    },
+    {
+      "epoch": 4.18,
+      "learning_rate": 1.32e-06,
+      "loss": 1.3915,
+      "step": 23
+    },
+    {
+      "epoch": 4.36,
+      "learning_rate": 1.3800000000000001e-06,
+      "loss": 1.3803,
+      "step": 24
+    },
+    {
+      "epoch": 4.55,
+      "learning_rate": 1.44e-06,
+      "loss": 1.3711,
+      "step": 25
+    },
+    {
+      "epoch": 4.73,
+      "learning_rate": 1.5e-06,
+      "loss": 1.3596,
+      "step": 26
+    },
+    {
+      "epoch": 4.91,
+      "learning_rate": 1.56e-06,
+      "loss": 1.3376,
+      "step": 27
+    },
+    {
+      "epoch": 5.09,
+      "learning_rate": 1.6200000000000002e-06,
+      "loss": 1.3241,
+      "step": 28
+    },
+    {
+      "epoch": 5.27,
+      "learning_rate": 1.6800000000000002e-06,
+      "loss": 1.2975,
+      "step": 29
+    },
+    {
+      "epoch": 5.45,
+      "learning_rate": 1.7399999999999999e-06,
+      "loss": 1.2847,
+      "step": 30
+    },
+    {
+      "epoch": 5.45,
+      "eval_loss": 1.4864555597305298,
+      "eval_runtime": 1.4609,
+      "eval_samples_per_second": 45.176,
+      "eval_steps_per_second": 2.053,
+      "step": 30
+    },
+    {
+      "epoch": 5.64,
+      "learning_rate": 1.8e-06,
+      "loss": 1.2798,
+      "step": 31
+    },
+    {
+      "epoch": 5.82,
+      "learning_rate": 1.86e-06,
+      "loss": 1.2616,
+      "step": 32
+    },
+    {
+      "epoch": 6.0,
+      "learning_rate": 1.9200000000000003e-06,
+      "loss": 1.252,
+      "step": 33
+    },
+    {
+      "epoch": 6.18,
+      "learning_rate": 1.98e-06,
+      "loss": 1.2398,
+      "step": 34
+    },
+    {
+      "epoch": 6.36,
+      "learning_rate": 2.0400000000000004e-06,
+      "loss": 1.2229,
+      "step": 35
+    },
+    {
+      "epoch": 6.55,
+      "learning_rate": 2.1e-06,
+      "loss": 1.2221,
+      "step": 36
+    },
+    {
+      "epoch": 6.73,
+      "learning_rate": 2.16e-06,
+      "loss": 1.2046,
+      "step": 37
+    },
+    {
+      "epoch": 6.91,
+      "learning_rate": 2.22e-06,
+      "loss": 1.1908,
+      "step": 38
+    },
+    {
+      "epoch": 7.09,
+      "learning_rate": 2.28e-06,
+      "loss": 1.1916,
+      "step": 39
+    },
+    {
+      "epoch": 7.27,
+      "learning_rate": 2.34e-06,
+      "loss": 1.1733,
+      "step": 40
+    },
+    {
+      "epoch": 7.27,
+      "eval_loss": 1.37030827999115,
+      "eval_runtime": 1.4628,
+      "eval_samples_per_second": 45.118,
+      "eval_steps_per_second": 2.051,
+      "step": 40
+    },
+    {
+      "epoch": 7.45,
+      "learning_rate": 2.4000000000000003e-06,
+      "loss": 1.1708,
+      "step": 41
+    },
+    {
+      "epoch": 7.64,
+      "learning_rate": 2.4599999999999997e-06,
+      "loss": 1.1727,
+      "step": 42
+    },
+    {
+      "epoch": 7.82,
+      "learning_rate": 2.52e-06,
+      "loss": 1.1615,
+      "step": 43
+    },
+    {
+      "epoch": 8.0,
+      "learning_rate": 2.58e-06,
+      "loss": 1.1529,
+      "step": 44
+    },
+    {
+      "epoch": 8.18,
+      "learning_rate": 2.64e-06,
+      "loss": 1.1428,
+      "step": 45
+    },
+    {
+      "epoch": 8.36,
+      "learning_rate": 2.7e-06,
+      "loss": 1.1398,
+      "step": 46
+    },
+    {
+      "epoch": 8.55,
+      "learning_rate": 2.7600000000000003e-06,
+      "loss": 1.1392,
+      "step": 47
+    },
+    {
+      "epoch": 8.73,
+      "learning_rate": 2.82e-06,
+      "loss": 1.121,
+      "step": 48
+    },
+    {
+      "epoch": 8.91,
+      "learning_rate": 2.88e-06,
+      "loss": 1.1239,
+      "step": 49
+    },
+    {
+      "epoch": 9.09,
+      "learning_rate": 2.9400000000000002e-06,
+      "loss": 1.1131,
+      "step": 50
+    },
+    {
+      "epoch": 9.09,
+      "eval_loss": 1.3062629699707031,
+      "eval_runtime": 1.4506,
+      "eval_samples_per_second": 45.499,
+      "eval_steps_per_second": 2.068,
+      "step": 50
+    },
+    {
+      "epoch": 9.27,
+      "learning_rate": 3e-06,
+      "loss": 1.1047,
+      "step": 51
+    },
+    {
+      "epoch": 9.45,
+      "learning_rate": 3.06e-06,
+      "loss": 1.1012,
+      "step": 52
+    },
+    {
+      "epoch": 9.64,
+      "learning_rate": 3.12e-06,
+      "loss": 1.0987,
+      "step": 53
+    },
+    {
+      "epoch": 9.82,
+      "learning_rate": 3.18e-06,
+      "loss": 1.1012,
+      "step": 54
+    },
+    {
+      "epoch": 10.0,
+      "learning_rate": 3.2400000000000003e-06,
+      "loss": 1.093,
+      "step": 55
+    },
+    {
+      "epoch": 10.18,
+      "learning_rate": 3.3e-06,
+      "loss": 1.0876,
+      "step": 56
+    },
+    {
+      "epoch": 10.36,
+      "learning_rate": 3.3600000000000004e-06,
+      "loss": 1.0813,
+      "step": 57
+    },
+    {
+      "epoch": 10.55,
+      "learning_rate": 3.42e-06,
+      "loss": 1.0798,
+      "step": 58
+    },
+    {
+      "epoch": 10.73,
+      "learning_rate": 3.4799999999999997e-06,
+      "loss": 1.0673,
+      "step": 59
+    },
+    {
+      "epoch": 10.91,
+      "learning_rate": 3.54e-06,
+      "loss": 1.0616,
+      "step": 60
+    },
+    {
+      "epoch": 10.91,
+      "eval_loss": 1.2495708465576172,
+      "eval_runtime": 1.4605,
+      "eval_samples_per_second": 45.19,
+      "eval_steps_per_second": 2.054,
+      "step": 60
+    },
+    {
+      "epoch": 11.09,
+      "learning_rate": 3.6e-06,
+      "loss": 1.0639,
+      "step": 61
+    },
+    {
+      "epoch": 11.27,
+      "learning_rate": 3.66e-06,
+      "loss": 1.0538,
+      "step": 62
+    },
+    {
+      "epoch": 11.45,
+      "learning_rate": 3.72e-06,
+      "loss": 1.0567,
+      "step": 63
+    },
+    {
+      "epoch": 11.64,
+      "learning_rate": 3.7800000000000002e-06,
+      "loss": 1.0465,
+      "step": 64
+    },
+    {
+      "epoch": 11.82,
+      "learning_rate": 3.8400000000000005e-06,
+      "loss": 1.0485,
+      "step": 65
+    },
+    {
+      "epoch": 12.0,
+      "learning_rate": 3.9e-06,
+      "loss": 1.0494,
+      "step": 66
+    },
+    {
+      "epoch": 12.18,
+      "learning_rate": 3.96e-06,
+      "loss": 1.0359,
+      "step": 67
+    },
+    {
+      "epoch": 12.36,
+      "learning_rate": 4.0200000000000005e-06,
+      "loss": 1.0336,
+      "step": 68
+    },
+    {
+      "epoch": 12.55,
+      "learning_rate": 4.080000000000001e-06,
+      "loss": 1.0317,
+      "step": 69
+    },
+    {
+      "epoch": 12.73,
+      "learning_rate": 4.14e-06,
+      "loss": 1.0302,
+      "step": 70
+    },
+    {
+      "epoch": 12.73,
+      "eval_loss": 1.2099440097808838,
+      "eval_runtime": 1.4611,
+      "eval_samples_per_second": 45.172,
+      "eval_steps_per_second": 2.053,
+      "step": 70
+    },
+    {
+      "epoch": 12.91,
+      "learning_rate": 4.2e-06,
+      "loss": 1.0249,
+      "step": 71
+    },
+    {
+      "epoch": 13.09,
+      "learning_rate": 4.26e-06,
+      "loss": 1.022,
+      "step": 72
+    },
+    {
+      "epoch": 13.27,
+      "learning_rate": 4.32e-06,
+      "loss": 1.0138,
+      "step": 73
+    },
+    {
+      "epoch": 13.45,
+      "learning_rate": 4.38e-06,
+      "loss": 1.0136,
+      "step": 74
+    },
+    {
+      "epoch": 13.64,
+      "learning_rate": 4.44e-06,
+      "loss": 1.0169,
+      "step": 75
+    },
+    {
+      "epoch": 13.82,
+      "learning_rate": 4.5e-06,
+      "loss": 1.0041,
+      "step": 76
+    },
+    {
+      "epoch": 14.0,
+      "learning_rate": 4.56e-06,
+      "loss": 0.9956,
+      "step": 77
+    },
+    {
+      "epoch": 14.18,
+      "learning_rate": 4.62e-06,
+      "loss": 0.9941,
+      "step": 78
+    },
+    {
+      "epoch": 14.36,
+      "learning_rate": 4.68e-06,
+      "loss": 0.9963,
+      "step": 79
+    },
+    {
+      "epoch": 14.55,
+      "learning_rate": 4.74e-06,
+      "loss": 0.9866,
+      "step": 80
+    },
+    {
+      "epoch": 14.55,
+      "eval_loss": 1.1811805963516235,
+      "eval_runtime": 1.4593,
+      "eval_samples_per_second": 45.228,
+      "eval_steps_per_second": 2.056,
+      "step": 80
+    },
+    {
+      "epoch": 14.73,
+      "learning_rate": 4.800000000000001e-06,
+      "loss": 0.9904,
+      "step": 81
+    },
+    {
+      "epoch": 14.91,
+      "learning_rate": 4.86e-06,
+      "loss": 0.9849,
+      "step": 82
+    },
+    {
+      "epoch": 15.09,
+      "learning_rate": 4.9199999999999995e-06,
+      "loss": 0.9784,
+      "step": 83
+    },
+    {
+      "epoch": 15.27,
+      "learning_rate": 4.98e-06,
+      "loss": 0.9801,
+      "step": 84
+    },
+    {
+      "epoch": 15.45,
+      "learning_rate": 5.04e-06,
+      "loss": 0.9749,
+      "step": 85
+    },
+    {
+      "epoch": 15.64,
+      "learning_rate": 5.1e-06,
+      "loss": 0.9667,
+      "step": 86
+    },
+    {
+      "epoch": 15.82,
+      "learning_rate": 5.16e-06,
+      "loss": 0.9675,
+      "step": 87
+    },
+    {
+      "epoch": 16.0,
+      "learning_rate": 5.22e-06,
+      "loss": 0.9666,
+      "step": 88
+    },
+    {
+      "epoch": 16.18,
+      "learning_rate": 5.28e-06,
+      "loss": 0.9582,
+      "step": 89
+    },
+    {
+      "epoch": 16.36,
+      "learning_rate": 5.3400000000000005e-06,
+      "loss": 0.9594,
+      "step": 90
+    },
+    {
+      "epoch": 16.36,
+      "eval_loss": 1.1599087715148926,
+      "eval_runtime": 1.4615,
+      "eval_samples_per_second": 45.16,
+      "eval_steps_per_second": 2.053,
+      "step": 90
+    },
+    {
+      "epoch": 16.55,
+      "learning_rate": 5.4e-06,
+      "loss": 0.9462,
+      "step": 91
+    },
+    {
+      "epoch": 16.73,
+      "learning_rate": 5.46e-06,
+      "loss": 0.952,
+      "step": 92
+    },
+    {
+      "epoch": 16.91,
+      "learning_rate": 5.5200000000000005e-06,
+      "loss": 0.9507,
+      "step": 93
+    },
+    {
+      "epoch": 17.09,
+      "learning_rate": 5.580000000000001e-06,
+      "loss": 0.9535,
+      "step": 94
+    },
+    {
+      "epoch": 17.27,
+      "learning_rate": 5.64e-06,
+      "loss": 0.9436,
+      "step": 95
+    },
+    {
+      "epoch": 17.45,
+      "learning_rate": 5.7e-06,
+      "loss": 0.9338,
+      "step": 96
+    },
+    {
+      "epoch": 17.64,
+      "learning_rate": 5.76e-06,
+      "loss": 0.9355,
+      "step": 97
+    },
+    {
+      "epoch": 17.82,
+      "learning_rate": 5.82e-06,
+      "loss": 0.9355,
+      "step": 98
+    },
+    {
+      "epoch": 18.0,
+      "learning_rate": 5.8800000000000005e-06,
+      "loss": 0.9343,
+      "step": 99
+    },
+    {
+      "epoch": 18.18,
+      "learning_rate": 5.94e-06,
+      "loss": 0.9161,
+      "step": 100
+    },
+    {
+      "epoch": 18.18,
+      "eval_loss": 1.1503610610961914,
+      "eval_runtime": 1.4537,
+      "eval_samples_per_second": 45.4,
+      "eval_steps_per_second": 2.064,
+      "step": 100
+    },
+    {
+      "epoch": 18.36,
+      "learning_rate": 6e-06,
+      "loss": 0.9156,
+      "step": 101
+    },
+    {
+      "epoch": 18.55,
+      "learning_rate": 5.94e-06,
+      "loss": 0.9185,
+      "step": 102
+    },
+    {
+      "epoch": 18.73,
+      "learning_rate": 5.8800000000000005e-06,
+      "loss": 0.9155,
+      "step": 103
+    },
+    {
+      "epoch": 18.91,
+      "learning_rate": 5.82e-06,
+      "loss": 0.9114,
+      "step": 104
+    },
+    {
+      "epoch": 19.09,
+      "learning_rate": 5.76e-06,
+      "loss": 0.8976,
+      "step": 105
+    },
+    {
+      "epoch": 19.27,
+      "learning_rate": 5.7e-06,
+      "loss": 0.8985,
+      "step": 106
+    },
+    {
+      "epoch": 19.45,
+      "learning_rate": 5.64e-06,
+      "loss": 0.8972,
+      "step": 107
+    },
+    {
+      "epoch": 19.64,
+      "learning_rate": 5.580000000000001e-06,
+      "loss": 0.8961,
+      "step": 108
+    },
+    {
+      "epoch": 19.82,
+      "learning_rate": 5.5200000000000005e-06,
+      "loss": 0.8948,
+      "step": 109
+    },
+    {
+      "epoch": 20.0,
+      "learning_rate": 5.46e-06,
+      "loss": 0.8823,
+      "step": 110
+    },
+    {
+      "epoch": 20.0,
+      "eval_loss": 1.13896906375885,
+      "eval_runtime": 1.4633,
+      "eval_samples_per_second": 45.103,
+      "eval_steps_per_second": 2.05,
+      "step": 110
+    },
+    {
+      "epoch": 20.18,
+      "learning_rate": 5.4e-06,
+      "loss": 0.8689,
+      "step": 111
+    },
+    {
+      "epoch": 20.36,
+      "learning_rate": 5.3400000000000005e-06,
+      "loss": 0.8832,
+      "step": 112
+    },
+    {
+      "epoch": 20.55,
+      "learning_rate": 5.28e-06,
+      "loss": 0.875,
+      "step": 113
+    },
+    {
+      "epoch": 20.73,
+      "learning_rate": 5.22e-06,
+      "loss": 0.8655,
+      "step": 114
+    },
+    {
+      "epoch": 20.91,
+      "learning_rate": 5.16e-06,
+      "loss": 0.8695,
+      "step": 115
+    },
+    {
+      "epoch": 21.09,
+      "learning_rate": 5.1e-06,
+      "loss": 0.8636,
+      "step": 116
+    },
+    {
+      "epoch": 21.27,
+      "learning_rate": 5.04e-06,
+      "loss": 0.8513,
+      "step": 117
+    },
+    {
+      "epoch": 21.45,
+      "learning_rate": 4.98e-06,
+      "loss": 0.8523,
+      "step": 118
+    },
+    {
+      "epoch": 21.64,
+      "learning_rate": 4.9199999999999995e-06,
+      "loss": 0.8497,
+      "step": 119
+    },
+    {
+      "epoch": 21.82,
+      "learning_rate": 4.86e-06,
+      "loss": 0.8441,
+      "step": 120
+    },
+    {
+      "epoch": 21.82,
+      "eval_loss": 1.1353310346603394,
+      "eval_runtime": 1.4613,
+      "eval_samples_per_second": 45.164,
+      "eval_steps_per_second": 2.053,
+      "step": 120
+    },
+    {
+      "epoch": 22.0,
+      "learning_rate": 4.800000000000001e-06,
+      "loss": 0.8543,
+      "step": 121
+    },
+    {
+      "epoch": 22.18,
+      "learning_rate": 4.74e-06,
+      "loss": 0.838,
+      "step": 122
+    },
+    {
+      "epoch": 22.36,
+      "learning_rate": 4.68e-06,
+      "loss": 0.8348,
+      "step": 123
+    },
+    {
+      "epoch": 22.55,
+      "learning_rate": 4.62e-06,
+      "loss": 0.8215,
+      "step": 124
+    },
+    {
+      "epoch": 22.73,
+      "learning_rate": 4.56e-06,
+      "loss": 0.8313,
+      "step": 125
+    },
+    {
+      "epoch": 22.91,
+      "learning_rate": 4.5e-06,
+      "loss": 0.8303,
+      "step": 126
+    },
+    {
+      "epoch": 23.09,
+      "learning_rate": 4.44e-06,
+      "loss": 0.8188,
+      "step": 127
+    },
+    {
+      "epoch": 23.27,
+      "learning_rate": 4.38e-06,
+      "loss": 0.812,
+      "step": 128
+    },
+    {
+      "epoch": 23.45,
+      "learning_rate": 4.32e-06,
+      "loss": 0.8077,
+      "step": 129
+    },
+    {
+      "epoch": 23.64,
+      "learning_rate": 4.26e-06,
+      "loss": 0.7956,
+      "step": 130
+    },
+    {
+      "epoch": 23.64,
+      "eval_loss": 1.1432788372039795,
+      "eval_runtime": 1.461,
+      "eval_samples_per_second": 45.176,
+      "eval_steps_per_second": 2.053,
+      "step": 130
+    },
+    {
+      "epoch": 23.82,
+      "learning_rate": 4.2e-06,
+      "loss": 0.8099,
+      "step": 131
+    },
+    {
+      "epoch": 24.0,
+      "learning_rate": 4.14e-06,
+      "loss": 0.8223,
+      "step": 132
+    },
+    {
+      "epoch": 24.18,
+      "learning_rate": 4.080000000000001e-06,
+      "loss": 0.7956,
+      "step": 133
+    },
+    {
+      "epoch": 24.36,
+      "learning_rate": 4.0200000000000005e-06,
+      "loss": 0.7889,
+      "step": 134
+    },
+    {
+      "epoch": 24.55,
+      "learning_rate": 3.96e-06,
+      "loss": 0.7844,
+      "step": 135
+    },
+    {
+      "epoch": 24.73,
+      "learning_rate": 3.9e-06,
+      "loss": 0.7867,
+      "step": 136
+    },
+    {
+      "epoch": 24.91,
+      "learning_rate": 3.8400000000000005e-06,
+      "loss": 0.7875,
+      "step": 137
+    },
+    {
+      "epoch": 25.09,
+      "learning_rate": 3.7800000000000002e-06,
+      "loss": 0.7804,
+      "step": 138
+    },
+    {
+      "epoch": 25.27,
+      "learning_rate": 3.72e-06,
+      "loss": 0.7661,
+      "step": 139
+    },
+    {
+      "epoch": 25.45,
+      "learning_rate": 3.66e-06,
+      "loss": 0.7696,
+      "step": 140
+    },
+    {
+      "epoch": 25.45,
+      "eval_loss": 1.1572602987289429,
+      "eval_runtime": 1.4603,
+      "eval_samples_per_second": 45.197,
+      "eval_steps_per_second": 2.054,
+      "step": 140
+    },
+    {
+      "epoch": 25.64,
+      "learning_rate": 3.6e-06,
+      "loss": 0.7531,
+      "step": 141
+    },
+    {
+      "epoch": 25.82,
+      "learning_rate": 3.54e-06,
+      "loss": 0.7538,
+      "step": 142
+    },
+    {
+      "epoch": 26.0,
+      "learning_rate": 3.4799999999999997e-06,
+      "loss": 0.7574,
+      "step": 143
+    },
+    {
+      "epoch": 26.18,
+      "learning_rate": 3.42e-06,
+      "loss": 0.746,
+      "step": 144
+    },
+    {
+      "epoch": 26.36,
+      "learning_rate": 3.3600000000000004e-06,
+      "loss": 0.742,
+      "step": 145
+    },
+    {
+      "epoch": 26.55,
+      "learning_rate": 3.3e-06,
+      "loss": 0.7371,
+      "step": 146
+    },
+    {
+      "epoch": 26.73,
+      "learning_rate": 3.2400000000000003e-06,
+      "loss": 0.7355,
+      "step": 147
+    },
+    {
+      "epoch": 26.91,
+      "learning_rate": 3.18e-06,
+      "loss": 0.7352,
+      "step": 148
+    },
+    {
+      "epoch": 27.09,
+      "learning_rate": 3.12e-06,
+      "loss": 0.7263,
+      "step": 149
+    },
+    {
+      "epoch": 27.27,
+      "learning_rate": 3.06e-06,
+      "loss": 0.7113,
+      "step": 150
+    },
+    {
+      "epoch": 27.27,
+      "eval_loss": 1.1966105699539185,
+      "eval_runtime": 1.4607,
+      "eval_samples_per_second": 45.185,
+      "eval_steps_per_second": 2.054,
+      "step": 150
+    },
+    {
+      "epoch": 27.45,
+      "learning_rate": 3e-06,
+      "loss": 0.7142,
+      "step": 151
+    },
+    {
+      "epoch": 27.64,
+      "learning_rate": 2.9400000000000002e-06,
+      "loss": 0.7189,
+      "step": 152
+    },
+    {
+      "epoch": 27.82,
+      "learning_rate": 2.88e-06,
+      "loss": 0.7131,
+      "step": 153
+    },
+    {
+      "epoch": 28.0,
+      "learning_rate": 2.82e-06,
+      "loss": 0.7054,
+      "step": 154
+    },
+    {
+      "epoch": 28.18,
+      "learning_rate": 2.7600000000000003e-06,
+      "loss": 0.6965,
+      "step": 155
+    },
+    {
+      "epoch": 28.36,
+      "learning_rate": 2.7e-06,
+      "loss": 0.6879,
+      "step": 156
+    },
+    {
+      "epoch": 28.55,
+      "learning_rate": 2.64e-06,
+      "loss": 0.6872,
+      "step": 157
+    },
+    {
+      "epoch": 28.73,
+      "learning_rate": 2.58e-06,
+      "loss": 0.6909,
+      "step": 158
+    },
+    {
+      "epoch": 28.91,
+      "learning_rate": 2.52e-06,
+      "loss": 0.6734,
+      "step": 159
+    },
+    {
+      "epoch": 29.09,
+      "learning_rate": 2.4599999999999997e-06,
+      "loss": 0.6646,
+      "step": 160
+    },
+    {
+      "epoch": 29.09,
+      "eval_loss": 1.2236099243164062,
+      "eval_runtime": 1.4503,
+      "eval_samples_per_second": 45.508,
+      "eval_steps_per_second": 2.069,
+      "step": 160
+    },
+    {
+      "epoch": 29.27,
+      "learning_rate": 2.4000000000000003e-06,
+      "loss": 0.6715,
+      "step": 161
+    },
+    {
+      "epoch": 29.45,
+      "learning_rate": 2.34e-06,
+      "loss": 0.6621,
+      "step": 162
+    },
+    {
+      "epoch": 29.64,
+      "learning_rate": 2.28e-06,
+      "loss": 0.6483,
+      "step": 163
+    },
+    {
+      "epoch": 29.82,
+      "learning_rate": 2.22e-06,
+      "loss": 0.6487,
+      "step": 164
+    },
+    {
+      "epoch": 30.0,
+      "learning_rate": 2.16e-06,
+      "loss": 0.6553,
+      "step": 165
+    },
+    {
+      "epoch": 30.18,
+      "learning_rate": 2.1e-06,
+      "loss": 0.6344,
+      "step": 166
+    },
+    {
+      "epoch": 30.36,
+      "learning_rate": 2.0400000000000004e-06,
+      "loss": 0.6334,
+      "step": 167
+    },
+    {
+      "epoch": 30.55,
+      "learning_rate": 1.98e-06,
+      "loss": 0.6311,
+      "step": 168
+    },
+    {
+      "epoch": 30.73,
+      "learning_rate": 1.9200000000000003e-06,
+      "loss": 0.6262,
+      "step": 169
+    },
+    {
+      "epoch": 30.91,
+      "learning_rate": 1.86e-06,
+      "loss": 0.6303,
+      "step": 170
+    },
+    {
+      "epoch": 30.91,
+      "eval_loss": 1.2599583864212036,
+      "eval_runtime": 1.4611,
+      "eval_samples_per_second": 45.171,
+      "eval_steps_per_second": 2.053,
+      "step": 170
+    },
+    {
+      "epoch": 31.09,
+      "learning_rate": 1.8e-06,
+      "loss": 0.6196,
+      "step": 171
+    },
+    {
+      "epoch": 31.27,
+      "learning_rate": 1.7399999999999999e-06,
+      "loss": 0.5957,
+      "step": 172
+    },
+    {
+      "epoch": 31.45,
+      "learning_rate": 1.6800000000000002e-06,
+      "loss": 0.5994,
+      "step": 173
+    },
+    {
+      "epoch": 31.64,
+      "learning_rate": 1.6200000000000002e-06,
+      "loss": 0.6109,
+      "step": 174
+    },
+    {
+      "epoch": 31.82,
+      "learning_rate": 1.56e-06,
+      "loss": 0.61,
+      "step": 175
+    },
+    {
+      "epoch": 32.0,
+      "learning_rate": 1.5e-06,
+      "loss": 0.5957,
+      "step": 176
+    },
+    {
+      "epoch": 32.18,
+      "learning_rate": 1.44e-06,
+      "loss": 0.5862,
+      "step": 177
+    },
+    {
+      "epoch": 32.36,
+      "learning_rate": 1.3800000000000001e-06,
+      "loss": 0.5799,
+      "step": 178
+    },
+    {
+      "epoch": 32.55,
+      "learning_rate": 1.32e-06,
+      "loss": 0.5788,
+      "step": 179
+    },
+    {
+      "epoch": 32.73,
+      "learning_rate": 1.26e-06,
+      "loss": 0.5741,
+      "step": 180
+    },
+    {
+      "epoch": 32.73,
+      "eval_loss": 1.3407286405563354,
+      "eval_runtime": 1.4616,
+      "eval_samples_per_second": 45.157,
+      "eval_steps_per_second": 2.053,
+      "step": 180
+    },
+    {
+      "epoch": 32.91,
+      "learning_rate": 1.2000000000000002e-06,
+      "loss": 0.5745,
+      "step": 181
+    },
+    {
+      "epoch": 33.09,
+      "learning_rate": 1.14e-06,
+      "loss": 0.5654,
+      "step": 182
+    },
+    {
+      "epoch": 33.27,
+      "learning_rate": 1.08e-06,
+      "loss": 0.5587,
+      "step": 183
+    },
+    {
+      "epoch": 33.45,
+      "learning_rate": 1.0200000000000002e-06,
+      "loss": 0.5539,
+      "step": 184
+    },
+    {
+      "epoch": 33.64,
+      "learning_rate": 9.600000000000001e-07,
+      "loss": 0.558,
+      "step": 185
+    },
+    {
+      "epoch": 33.82,
+      "learning_rate": 9e-07,
+      "loss": 0.5571,
+      "step": 186
+    },
+    {
+      "epoch": 34.0,
+      "learning_rate": 8.400000000000001e-07,
+      "loss": 0.5496,
+      "step": 187
+    }
+  ],
+  "logging_steps": 1,
+  "max_steps": 200,
+  "num_train_epochs": 40,
+  "save_steps": 500,
+  "total_flos": 6.274288047202763e+18,
+  "trial_name": null,
+  "trial_params": null
+}

training_args.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:164da52fddefcb4fa95359f4d25b6fb04b19f1c27f0445d05b814bea4965d4b4
+size 5947

zero_to_fp32.py

@@ -0,0 +1,587 @@
+#!/usr/bin/env python
+
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
+# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
+# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
+# application.
+#
+# example: python zero_to_fp32.py . pytorch_model.bin
+
+import argparse
+import torch
+import glob
+import math
+import os
+import re
+from collections import OrderedDict
+from dataclasses import dataclass
+
+# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
+# DeepSpeed data structures it has to be available in the current python environment.
+from deepspeed.utils import logger
+from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
+                                            FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
+                                            FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
+
+
+@dataclass
+class zero_model_state:
+    buffers: dict()
+    param_shapes: dict()
+    shared_params: list
+    ds_version: int
+    frozen_param_shapes: dict()
+    frozen_param_fragments: dict()
+
+
+debug = 0
+
+# load to cpu
+device = torch.device('cpu')
+
+
+def atoi(text):
+    return int(text) if text.isdigit() else text
+
+
+def natural_keys(text):
+    '''
+    alist.sort(key=natural_keys) sorts in human order
+    http://nedbatchelder.com/blog/200712/human_sorting.html
+    (See Toothy's implementation in the comments)
+    '''
+    return [atoi(c) for c in re.split(r'(\d+)', text)]
+
+
+def get_model_state_file(checkpoint_dir, zero_stage):
+    if not os.path.isdir(checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
+
+    # there should be only one file
+    if zero_stage <= 2:
+        file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
+    elif zero_stage == 3:
+        file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
+
+    if not os.path.exists(file):
+        raise FileNotFoundError(f"can't find model states file at '{file}'")
+
+    return file
+
+
+def get_checkpoint_files(checkpoint_dir, glob_pattern):
+    # XXX: need to test that this simple glob rule works for multi-node setup too
+    ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
+
+    if len(ckpt_files) == 0:
+        raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
+
+    return ckpt_files
+
+
+def get_optim_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
+
+
+def get_model_state_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device)
+
+        if BUFFER_NAMES not in state_dict:
+            raise ValueError(f"{file} is not a model state checkpoint")
+        buffer_names = state_dict[BUFFER_NAMES]
+        if debug:
+            print("Found buffers:", buffer_names)
+
+        # recover just the buffers while restoring them to fp32 if they were saved in fp16
+        buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
+        param_shapes = state_dict[PARAM_SHAPES]
+
+        # collect parameters that are included in param_shapes
+        param_names = []
+        for s in param_shapes:
+            for name in s.keys():
+                param_names.append(name)
+
+        # update with frozen parameters
+        frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
+        if frozen_param_shapes is not None:
+            if debug:
+                print(f"Found frozen_param_shapes: {frozen_param_shapes}")
+            param_names += list(frozen_param_shapes.keys())
+
+        # handle shared params
+        shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
+
+        ds_version = state_dict.get(DS_VERSION, None)
+
+        frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
+
+        z_model_state = zero_model_state(buffers=buffers,
+                                         param_shapes=param_shapes,
+                                         shared_params=shared_params,
+                                         ds_version=ds_version,
+                                         frozen_param_shapes=frozen_param_shapes,
+                                         frozen_param_fragments=frozen_param_fragments)
+        zero_model_states.append(z_model_state)
+
+    return zero_model_states
+
+
+def parse_optim_states(files, ds_checkpoint_dir):
+
+    total_files = len(files)
+    state_dicts = []
+    for f in files:
+        state_dict = torch.load(f, map_location=device)
+        # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
+        # and also handle the case where it was already removed by another helper script
+        state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
+        state_dicts.append(state_dict)
+
+    if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
+        raise ValueError(f"{files[0]} is not a zero checkpoint")
+    zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
+    world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
+
+    # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
+    # parameters can be different from data parallelism for non-expert parameters. So we can just
+    # use the max of the partition_count to get the dp world_size.
+
+    if type(world_size) is list:
+        world_size = max(world_size)
+
+    if world_size != total_files:
+        raise ValueError(
+            f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
+            "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
+        )
+
+    # the groups are named differently in each stage
+    if zero_stage <= 2:
+        fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
+    elif zero_stage == 3:
+        fp32_groups_key = FP32_FLAT_GROUPS
+    else:
+        raise ValueError(f"unknown zero stage {zero_stage}")
+
+    if zero_stage <= 2:
+        fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
+    elif zero_stage == 3:
+        # if there is more than one param group, there will be multiple flattened tensors - one
+        # flattened tensor per group - for simplicity merge them into a single tensor
+        #
+        # XXX: could make the script more memory efficient for when there are multiple groups - it
+        # will require matching the sub-lists of param_shapes for each param group flattened tensor
+
+        fp32_flat_groups = [
+            torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
+        ]
+
+    return zero_stage, world_size, fp32_flat_groups
+
+
+def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir):
+    """
+    Returns fp32 state_dict reconstructed from ds checkpoint
+
+    Args:
+        - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
+
+    """
+    print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
+
+    optim_files = get_optim_files(ds_checkpoint_dir)
+    zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
+    print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
+
+    model_files = get_model_state_files(ds_checkpoint_dir)
+
+    zero_model_states = parse_model_states(model_files)
+    print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
+
+    if zero_stage <= 2:
+        return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states)
+    elif zero_stage == 3:
+        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states)
+
+
+def _zero2_merge_frozen_params(state_dict, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+    frozen_param_fragments = zero_model_states[0].frozen_param_fragments
+
+    if debug:
+        num_elem = sum(s.numel() for s in frozen_param_shapes.values())
+        print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        state_dict[name] = frozen_param_fragments[name]
+
+        if debug:
+            print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+
+    # Reconstruction protocol:
+    #
+    # XXX: document this
+
+    if debug:
+        for i in range(world_size):
+            for j in range(len(fp32_flat_groups[0])):
+                print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
+
+    # XXX: memory usage doubles here (zero2)
+    num_param_groups = len(fp32_flat_groups[0])
+    merged_single_partition_of_fp32_groups = []
+    for i in range(num_param_groups):
+        merged_partitions = [sd[i] for sd in fp32_flat_groups]
+        full_single_fp32_vector = torch.cat(merged_partitions, 0)
+        merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
+    avail_numel = sum(
+        [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
+
+    if debug:
+        wanted_params = sum([len(shapes) for shapes in param_shapes])
+        wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
+        # not asserting if there is a mismatch due to possible padding
+        print(f"Have {avail_numel} numels to process.")
+        print(f"Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    total_numel = 0
+    total_params = 0
+    for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
+        offset = 0
+        avail_numel = full_single_fp32_vector.numel()
+        for name, shape in shapes.items():
+
+            unpartitioned_numel = shape.numel()
+            total_numel += unpartitioned_numel
+            total_params += 1
+
+            if debug:
+                print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+            state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
+            offset += unpartitioned_numel
+
+        # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
+        # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
+        # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
+        # live optimizer object, so we are checking that the numbers are within the right range
+        align_to = 2 * world_size
+
+        def zero2_align(x):
+            return align_to * math.ceil(x / align_to)
+
+        if debug:
+            print(f"original offset={offset}, avail_numel={avail_numel}")
+
+        offset = zero2_align(offset)
+        avail_numel = zero2_align(avail_numel)
+
+        if debug:
+            print(f"aligned  offset={offset}, avail_numel={avail_numel}")
+
+        # Sanity check
+        if offset != avail_numel:
+            raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    _zero2_merge_frozen_params(state_dict, zero_model_states)
+
+    _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def zero3_partitioned_param_info(unpartitioned_numel, world_size):
+    remainder = unpartitioned_numel % world_size
+    padding_numel = (world_size - remainder) if remainder else 0
+    partitioned_numel = math.ceil(unpartitioned_numel / world_size)
+    return partitioned_numel, padding_numel
+
+
+def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    if debug:
+        for i in range(world_size):
+            num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
+            print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in zero_model_states[0].frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
+        state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
+
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+    avail_numel = fp32_flat_groups[0].numel() * world_size
+    # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
+    # param, re-consolidating each param, while dealing with padding if any
+
+    # merge list of dicts, preserving order
+    param_shapes = {k: v for d in param_shapes for k, v in d.items()}
+
+    if debug:
+        for i in range(world_size):
+            print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
+
+        wanted_params = len(param_shapes)
+        wanted_numel = sum(shape.numel() for shape in param_shapes.values())
+        # not asserting if there is a mismatch due to possible padding
+        avail_numel = fp32_flat_groups[0].numel() * world_size
+        print(f"Trainable params: Have {avail_numel} numels to process.")
+        print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    offset = 0
+    total_numel = 0
+    total_params = 0
+    for name, shape in param_shapes.items():
+
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+        total_params += 1
+
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+        # XXX: memory usage doubles here
+        state_dict[name] = torch.cat(
+            tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
+            0).narrow(0, 0, unpartitioned_numel).view(shape)
+        offset += partitioned_numel
+
+    offset *= world_size
+
+    # Sanity check
+    if offset != avail_numel:
+        raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
+
+    _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
+    ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
+    via a model hub.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    Note: this approach may not work if your application doesn't have sufficient free CPU memory and
+    you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint.
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        # do the training and checkpoint saving
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
+        model = model.cpu() # move to cpu
+        model.load_state_dict(state_dict)
+        # submit to model hub or save the model to share with others
+
+    In this example the ``model`` will no longer be usable in the deepspeed context of the same
+    application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
+
+    """
+    if tag is None:
+        latest_path = os.path.join(checkpoint_dir, 'latest')
+        if os.path.isfile(latest_path):
+            with open(latest_path, 'r') as fd:
+                tag = fd.read().strip()
+        else:
+            raise ValueError(f"Unable to find 'latest' file at {latest_path}")
+
+    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
+
+    if not os.path.isdir(ds_checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
+
+    return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir)
+
+
+def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
+    loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+    """
+
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+    print(f"Saving fp32 state dict to {output_file}")
+    torch.save(state_dict, output_file)
+
+
+def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
+    """
+    1. Put the provided model to cpu
+    2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
+    3. Load it into the provided model
+
+    Args:
+        - ``model``: the model object to update
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+
+    Returns:
+        - ``model`: modified model
+
+    Make sure you have plenty of CPU memory available before you call this function. If you don't
+    have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
+    conveniently placed for you in the checkpoint folder.
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
+        model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
+        # submit to model hub or save the model to share with others
+
+    Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
+    of the same application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    """
+    logger.info(f"Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info(f"Overwriting model with fp32 weights")
+    model = model.cpu()
+    model.load_state_dict(state_dict, strict=False)
+
+    return model
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("checkpoint_dir",
+                        type=str,
+                        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
+    parser.add_argument(
+        "output_file",
+        type=str,
+        help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
+    parser.add_argument("-t",
+                        "--tag",
+                        type=str,
+                        default=None,
+                        help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
+    parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
+    args = parser.parse_args()
+
+    debug = args.debug
+
+    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir, args.output_file, tag=args.tag)