fbaldassarri
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Initial Upload
Browse files- README.md +86 -3
- config.json +40 -0
- generation_config.json +7 -0
- model.safetensors +3 -0
- quantization_config.json +15 -0
- special_tokens_map.json +23 -0
- tokenizer.json +0 -0
- tokenizer_config.json +214 -0
README.md
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---
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---
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language:
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- en
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tags:
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- pytorch
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- causal-lm
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- pythia
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- autoround
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- intel
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- intel-autoround
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- awq
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- autoawq
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- woq
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license: apache-2.0
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model_name: Pythia 1.4b
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base_model: EleutherAI/pythia-1.4b
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inference: false
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model_creator: EleutherAI
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datasets:
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- EleutherAI/pile
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pipeline_tag: text-generation
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prompt_template: '{prompt}
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'
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quantized_by: fbaldassarri
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---
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## Model Information
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Quantized version of [EleutherAI/pythia-1.4b](EleutherAI/pythia-1.4b) using torch.float32 for quantization tuning.
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- 4 bits (INT4)
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- group size = 128
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- Asymmetrical Quantization
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- Method AutoAWQ
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Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round)
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Note: this INT4 version of pythia-1.4b has been quantized to run inference through CPU.
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## Replication Recipe
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### Step 1 Install Requirements
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I suggest to install requirements into a dedicated python-virtualenv or a conda enviroment.
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```
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python -m pip install <package> --upgrade
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```
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- accelerate==1.0.1
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- auto_gptq==0.7.1
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- neural_compressor==3.1
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- torch==2.3.0+cpu
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- torchaudio==2.5.0+cpu
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- torchvision==0.18.0+cpu
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- transformers==4.45.2
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### Step 2 Build Intel Autoround wheel from sources
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```
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python -m pip install git+https://github.com/intel/auto-round.git
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```
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### Step 3 Script for Quantization
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```
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "EleutherAI/pythia-1.4b"
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model = AutoModelForCausalLM.from_pretrained(model_name)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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from auto_round import AutoRound
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bits, group_size, sym = 4, 128, False
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autoround = AutoRound(model, tokenizer, nsamples=128, iters=200, seqlen=512, batch_size=4, bits=bits, group_size=group_size, sym=sym)
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autoround.quantize()
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output_dir = "./AutoRound/EleutherAI_pythia-1.4b-autoawq-int4-gs128-asym"
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autoround.save_quantized(output_dir, format='auto_awq', inplace=True)
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```
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## License
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[Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
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## Disclaimer
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This quantized model comes with no warrenty. It has been developed only for research purposes.
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config.json
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{
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"_name_or_path": "EleutherAI/pythia-1.4b",
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"architectures": [
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"GPTNeoXForCausalLM"
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],
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"attention_bias": true,
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"classifier_dropout": 0.1,
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"eos_token_id": 0,
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"hidden_act": "gelu",
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"hidden_dropout": 0.0,
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"layer_norm_eps": 1e-05,
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"max_position_embeddings": 2048,
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"model_type": "gpt_neox",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"partial_rotary_factor": 0.25,
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"quantization_config": {
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"bits": 4,
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"group_size": 128,
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"modules_to_not_convert": null,
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"quant_method": "awq",
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"version": "gemm",
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"zero_point": true
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},
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"rope_scaling": null,
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"rope_theta": 10000,
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"rotary_emb_base": 10000,
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"rotary_pct": 0.25,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.45.2",
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"use_cache": true,
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"use_parallel_residual": true,
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"vocab_size": 50304
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 0,
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"do_sample": true,
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"eos_token_id": 0,
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"transformers_version": "4.45.2"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:d8bcd0a72252cd93e3898108523e4c0729c0b979444c5b8dc01d3619b2d869d6
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size 1453498176
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quantization_config.json
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{
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"quant_method": "awq",
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"modules_to_not_convert": null,
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"version": "gemm",
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"iters": 200,
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"lr": 0.005,
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"minmax_lr": 0.005,
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"enable_minmax_tuning": true,
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"enable_quanted_input": true,
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"scale_dtype": "float16",
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"sym": false,
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"bits": 4,
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"group_size": 128,
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"zero_point": true
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}
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
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"0": {
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"content": "<|endoftext|>",
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},
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