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---
base_model: cognitivecomputations/mixtral-1x22b-base
tags:
- generated_from_trainer
model-index:
- name: 1x22b-out
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: cognitivecomputations/mixtral-1x22b-base
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# trust_remote_code: true
# load_in_8bit: true
# load_in_4bit: true
# strict: false
datasets:
- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
type: sharegpt
conversation: chatml
chat_template: chatml
dataset_prepared_path: yi34b-prepared
val_set_size: 0.01
output_dir: ./1x22b-out
# adapter: qlora
# lora_r: 16
# lora_alpha: 16
# lora_modules_to_save: [embed_tokens, lm_head]
# lora_dropout: 0.05
# lora_target_linear: true
# unfrozen_parameters:
# - ^lm_head.weight$
# - ^model.embed_tokens.weight$
# # input_layernorm layers
# - model.layers.0.input_layernorm
# - model.layers.1.input_layernorm
# - model.layers.2.input_layernorm
# - model.layers.3.input_layernorm
# - model.layers.4.input_layernorm
# - model.layers.5.input_layernorm
# - model.layers.6.input_layernorm
# - model.layers.7.input_layernorm
# - model.layers.8.input_layernorm
# - model.layers.9.input_layernorm
# - model.layers.10.input_layernorm
# - model.layers.11.input_layernorm
# - model.layers.12.input_layernorm
# - model.layers.13.input_layernorm
# - model.layers.14.input_layernorm
# - model.layers.15.input_layernorm
# - model.layers.16.input_layernorm
# - model.layers.17.input_layernorm
# - model.layers.18.input_layernorm
# - model.layers.19.input_layernorm
# - model.layers.20.input_layernorm
# - model.layers.21.input_layernorm
# - model.layers.22.input_layernorm
# - model.layers.23.input_layernorm
# # lm_head layers
# # mlp.down_proj layers
# - model.layers.17.mlp.down_proj
# - model.layers.18.mlp.down_proj
# - model.layers.19.mlp.down_proj
# - model.layers.20.mlp.down_proj
# - model.layers.21.mlp.down_proj
# - model.layers.22.mlp.down_proj
# - model.layers.23.mlp.down_proj
# - model.layers.24.mlp.down_proj
# - model.layers.25.mlp.down_proj
# - model.layers.26.mlp.down_proj
# - model.layers.27.mlp.down_proj
# - model.layers.28.mlp.down_proj
# - model.layers.29.mlp.down_proj
# - model.layers.30.mlp.down_proj
# - model.layers.31.mlp.down_proj
# - model.layers.32.mlp.down_proj
# - model.layers.33.mlp.down_proj
# - model.layers.34.mlp.down_proj
# - model.layers.35.mlp.down_proj
# - model.layers.36.mlp.down_proj
# - model.layers.37.mlp.down_proj
# - model.layers.38.mlp.down_proj
# - model.layers.39.mlp.down_proj
# - model.layers.40.mlp.down_proj
# # mlp.gate_proj layers
# - model.layers.51.mlp.gate_proj
# - model.layers.50.mlp.gate_proj
# - model.layers.53.mlp.gate_proj
# - model.layers.52.mlp.gate_proj
# - model.layers.49.mlp.gate_proj
# - model.layers.45.mlp.gate_proj
# - model.layers.46.mlp.gate_proj
# - model.layers.47.mlp.gate_proj
# - model.layers.57.mlp.gate_proj
# - model.layers.48.mlp.gate_proj
# - model.layers.56.mlp.gate_proj
# - model.layers.41.mlp.gate_proj
# - model.layers.54.mlp.gate_proj
# - model.layers.43.mlp.gate_proj
# - model.layers.44.mlp.gate_proj
# - model.layers.60.mlp.gate_proj
# - model.layers.55.mlp.gate_proj
# - model.layers.40.mlp.gate_proj
# - model.layers.42.mlp.gate_proj
# - model.layers.58.mlp.gate_proj
# - model.layers.36.mlp.gate_proj
# - model.layers.37.mlp.gate_proj
# - model.layers.38.mlp.gate_proj
# - model.layers.39.mlp.gate_proj
# # mlp.up_proj layers
# - model.layers.50.mlp.up_proj
# - model.layers.51.mlp.up_proj
# - model.layers.41.mlp.up_proj
# - model.layers.49.mlp.up_proj
# - model.layers.43.mlp.up_proj
# - model.layers.44.mlp.up_proj
# - model.layers.40.mlp.up_proj
# - model.layers.45.mlp.up_proj
# - model.layers.47.mlp.up_proj
# - model.layers.48.mlp.up_proj
# - model.layers.46.mlp.up_proj
# - model.layers.42.mlp.up_proj
# - model.layers.39.mlp.up_proj
# - model.layers.36.mlp.up_proj
# - model.layers.37.mlp.up_proj
# - model.layers.38.mlp.up_proj
# - model.layers.56.mlp.up_proj
# - model.layers.57.mlp.up_proj
# - model.layers.53.mlp.up_proj
# - model.layers.31.mlp.up_proj
# - model.layers.32.mlp.up_proj
# - model.layers.34.mlp.up_proj
# - model.layers.35.mlp.up_proj
# - model.layers.33.mlp.up_proj
# # model.embed_tokens layers
# # model.norm layers
# # post_attention_layernorm layers
# - model.layers.0.post_attention_layernorm
# - model.layers.1.post_attention_layernorm
# - model.layers.2.post_attention_layernorm
# - model.layers.3.post_attention_layernorm
# - model.layers.4.post_attention_layernorm
# - model.layers.5.post_attention_layernorm
# - model.layers.6.post_attention_layernorm
# - model.layers.7.post_attention_layernorm
# - model.layers.8.post_attention_layernorm
# - model.layers.9.post_attention_layernorm
# - model.layers.10.post_attention_layernorm
# - model.layers.11.post_attention_layernorm
# - model.layers.12.post_attention_layernorm
# - model.layers.13.post_attention_layernorm
# - model.layers.14.post_attention_layernorm
# - model.layers.15.post_attention_layernorm
# - model.layers.16.post_attention_layernorm
# - model.layers.17.post_attention_layernorm
# - model.layers.18.post_attention_layernorm
# - model.layers.19.post_attention_layernorm
# - model.layers.20.post_attention_layernorm
# - model.layers.21.post_attention_layernorm
# - model.layers.22.post_attention_layernorm
# - model.layers.23.post_attention_layernorm
# # self_attn.k_proj layers
# - model.layers.42.self_attn.k_proj
# - model.layers.41.self_attn.k_proj
# - model.layers.39.self_attn.k_proj
# - model.layers.35.self_attn.k_proj
# - model.layers.28.self_attn.k_proj
# - model.layers.79.self_attn.k_proj
# - model.layers.43.self_attn.k_proj
# - model.layers.32.self_attn.k_proj
# - model.layers.73.self_attn.k_proj
# - model.layers.31.self_attn.k_proj
# - model.layers.29.self_attn.k_proj
# - model.layers.76.self_attn.k_proj
# - model.layers.30.self_attn.k_proj
# - model.layers.40.self_attn.k_proj
# - model.layers.33.self_attn.k_proj
# - model.layers.78.self_attn.k_proj
# - model.layers.34.self_attn.k_proj
# - model.layers.37.self_attn.k_proj
# - model.layers.45.self_attn.k_proj
# - model.layers.44.self_attn.k_proj
# - model.layers.71.self_attn.k_proj
# - model.layers.26.self_attn.k_proj
# - model.layers.74.self_attn.k_proj
# - model.layers.27.self_attn.k_proj
# # self_attn.o_proj layers
# - model.layers.35.self_attn.o_proj
# - model.layers.34.self_attn.o_proj
# - model.layers.37.self_attn.o_proj
# - model.layers.33.self_attn.o_proj
# - model.layers.31.self_attn.o_proj
# - model.layers.27.self_attn.o_proj
# - model.layers.38.self_attn.o_proj
# - model.layers.24.self_attn.o_proj
# - model.layers.39.self_attn.o_proj
# - model.layers.43.self_attn.o_proj
# - model.layers.29.self_attn.o_proj
# - model.layers.0.self_attn.o_proj
# - model.layers.50.self_attn.o_proj
# - model.layers.32.self_attn.o_proj
# - model.layers.45.self_attn.o_proj
# - model.layers.30.self_attn.o_proj
# - model.layers.60.self_attn.o_proj
# - model.layers.23.self_attn.o_proj
# - model.layers.18.self_attn.o_proj
# - model.layers.67.self_attn.o_proj
# - model.layers.57.self_attn.o_proj
# - model.layers.20.self_attn.o_proj
# - model.layers.76.self_attn.o_proj
# - model.layers.28.self_attn.o_proj
# # self_attn.q_proj layers
# - model.layers.1.self_attn.q_proj
# - model.layers.6.self_attn.q_proj
# - model.layers.0.self_attn.q_proj
# - model.layers.5.self_attn.q_proj
# - model.layers.2.self_attn.q_proj
# - model.layers.7.self_attn.q_proj
# - model.layers.3.self_attn.q_proj
# - model.layers.4.self_attn.q_proj
# - model.layers.8.self_attn.q_proj
# - model.layers.9.self_attn.q_proj
# - model.layers.61.self_attn.q_proj
# - model.layers.10.self_attn.q_proj
# - model.layers.62.self_attn.q_proj
# - model.layers.36.self_attn.q_proj
# - model.layers.15.self_attn.q_proj
# - model.layers.11.self_attn.q_proj
# - model.layers.17.self_attn.q_proj
# - model.layers.60.self_attn.q_proj
# - model.layers.63.self_attn.q_proj
# - model.layers.64.self_attn.q_proj
# - model.layers.29.self_attn.q_proj
# - model.layers.30.self_attn.q_proj
# - model.layers.55.self_attn.q_proj
# - model.layers.34.self_attn.q_proj
# # self_attn.v_proj layers
# - model.layers.12.self_attn.v_proj
# - model.layers.16.self_attn.v_proj
# - model.layers.18.self_attn.v_proj
# - model.layers.19.self_attn.v_proj
# - model.layers.20.self_attn.v_proj
# - model.layers.21.self_attn.v_proj
# - model.layers.22.self_attn.v_proj
# - model.layers.23.self_attn.v_proj
# - model.layers.24.self_attn.v_proj
# - model.layers.25.self_attn.v_proj
# - model.layers.26.self_attn.v_proj
# - model.layers.27.self_attn.v_proj
# - model.layers.28.self_attn.v_proj
# - model.layers.29.self_attn.v_proj
# - model.layers.30.self_attn.v_proj
# - model.layers.31.self_attn.v_proj
# - model.layers.32.self_attn.v_proj
# - model.layers.33.self_attn.v_proj
# - model.layers.34.self_attn.v_proj
# - model.layers.35.self_attn.v_proj
# - model.layers.36.self_attn.v_proj
# - model.layers.37.self_attn.v_proj
# - model.layers.38.self_attn.v_proj
# - model.layers.39.self_attn.v_proj
sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true
# adapter: lora
# lora_model_dir:
# lora_r: 32
# lora_alpha: 16
# lora_dropout: 0.05
# lora_target_linear: true
# lora_fan_in_fan_out:
wandb_project: dolphin-mixtral1x22b
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 3
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 1e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint: /workspace/axolotl2/axolotl/1x22b-out/checkpoint-507
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 4
save_total_limit: 2
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
weight_decay: 0.01
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
bos_token: "<s>"
# pad_token: "<unk>"
unk_token: "<unk>"
tokens:
- "<|im_start|>"
```
</details><br>
# 1x22b-out
This model is a fine-tuned version of [cognitivecomputations/mixtral-1x22b-base](https://huggingface.co/cognitivecomputations/mixtral-1x22b-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4572
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9818 | 0.0015 | 1 | 0.9854 |
| 0.4783 | 0.2499 | 169 | 0.5042 |
| 0.464 | 0.4997 | 338 | 0.4755 |
| 0.4561 | 0.7496 | 507 | 0.4593 |
| 0.3981 | 0.9994 | 676 | 0.4553 |
| 0.3725 | 1.2378 | 845 | 0.4525 |
| 0.3624 | 1.4877 | 1014 | 0.4457 |
| 0.359 | 1.7376 | 1183 | 0.4393 |
| 0.375 | 1.9874 | 1352 | 0.4345 |
| 0.2899 | 2.2260 | 1521 | 0.4488 |
| 0.2848 | 2.4759 | 1690 | 0.4473 |
| 0.2935 | 2.7257 | 1859 | 0.4470 |
| 0.2065 | 2.9756 | 2028 | 0.4572 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|