Initial Upload

README.md

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----
-license: apache-2.0
----
+---
+language:
+- en
+tags:
+- pytorch
+- causal-lm
+- pythia
+- autoround
+- intel
+- intel-autoround
+- awq
+- autoawq
+- auto-awq
+- woq
+license: apache-2.0
+model_name: Pythia 6.9b
+base_model: EleutherAI/pythia-6.9b
+inference: false
+model_creator: EleutherAI
+datasets:
+- EleutherAI/pile
+pipeline_tag: text-generation
+prompt_template: '{prompt}
+  '
+quantized_by: fbaldassarri
+---
+
+## Model Information
+
+Quantized version of [EleutherAI/pythia-6.9b](https://huggingface.co/EleutherAI/pythia-6.9b) using torch.float32 for quantization tuning.
+- 4 bits (INT4)
+- group size = 128
+- Asymmetrical Quantization
+- Method AutoAWQ
+
+Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.2 
+
+Note: this INT4 version of pythia-6.9b has been quantized to run inference through CPU.
+
+## Replication Recipe
+
+### Step 1 Install Requirements
+
+I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment. 
+
+```
+python -m pip install <package> --upgrade
+```
+
+- accelerate==1.2.0
+- autoawq==0.2.7.post3
+- auto_gptq==0.7.1
+- neural_compressor==3.1.1
+- torch==2.4.1+cpu
+- torchaudio==2.4.1+cpu
+- torchvision==0.19.1+cpu
+- transformers==4.47.0
+
+### Step 2 Build Intel Autoround wheel from sources
+
+```
+python -m pip install git+https://github.com/intel/auto-round.git
+```
+
+### Step 3 Script for Quantization
+
+```
+  from transformers import AutoModelForCausalLM, AutoTokenizer
+  model_name = "EleutherAI/pythia-6.9b"
+  model = AutoModelForCausalLM.from_pretrained(model_name)
+  tokenizer = AutoTokenizer.from_pretrained(model_name)
+  from auto_round import AutoRound
+  bits, group_size, sym, device, amp = 4, 128, False, 'cpu', False
+  autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym, device=device, amp=amp)
+  autoround.quantize()
+  output_dir = "./AutoRound/EleutherAI_pythia-6.9b-autoawq-int4-gs128-asym"
+  autoround.save_quantized(output_dir, format='auto_awq', inplace=True)
+```
+
+## License
+
+[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
+
+## Disclaimer
+
+This quantized model comes with no warrenty. It has been developed only for research purposes. 

config.json

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+{
+  "_name_or_path": "EleutherAI/pythia-6.9b",
+  "architectures": [
+    "GPTNeoXForCausalLM"
+  ],
+  "attention_bias": true,
+  "attention_dropout": 0.0,
+  "bos_token_id": 0,
+  "classifier_dropout": 0.1,
+  "eos_token_id": 0,
+  "hidden_act": "gelu",
+  "hidden_dropout": 0.0,
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 16384,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 2048,
+  "model_type": "gpt_neox",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "partial_rotary_factor": 0.25,
+  "quantization_config": {
+    "amp": false,
+    "autoround_version": "0.4.2",
+    "batch_size": 4,
+    "bits": 4,
+    "data_type": "int",
+    "dataset": "NeelNanda/pile-10k",
+    "enable_minmax_tuning": true,
+    "enable_norm_bias_tuning": false,
+    "enable_quanted_input": true,
+    "gradient_accumulate_steps": 1,
+    "group_size": 128,
+    "iters": 200,
+    "low_gpu_mem_usage": false,
+    "lr": 0.005,
+    "minmax_lr": 0.005,
+    "modules_to_not_convert": [
+      "embed_out"
+    ],
+    "nsamples": 128,
+    "quant_method": "awq",
+    "scale_dtype": "torch.float16",
+    "seqlen": 512,
+    "sym": false,
+    "to_quant_block_names": null,
+    "version": "gemm",
+    "zero_point": true
+  },
+  "rope_scaling": null,
+  "rope_theta": 10000,
+  "rotary_emb_base": 10000,
+  "rotary_pct": 0.25,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.0",
+  "use_cache": true,
+  "use_parallel_residual": true,
+  "vocab_size": 50432
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 0,
+  "transformers_version": "4.47.0"
+}

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quantization_config.json

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special_tokens_map.json

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