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fbaldassarri
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ibm-granite_granite-3.3-2b-base-autogptq-int4-gs64-asym

Text Generation
Transformers
Safetensors
PyTorch
granite
granite-3.3
autoround
auto-round
intel-autoround
intel
woq
gptq
autogptq
auto-gptq
ibm
granite-3
4-bit precision
Model card Files Community
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  1. README.md +93 -0
  2. config.json +64 -0
  3. generation_config.json +7 -0
  4. merges.txt +0 -0
  5. model.safetensors +3 -0
  6. quantize_config.json +32 -0
  7. special_tokens_map.json +51 -0
  8. tokenizer.json +0 -0
  9. tokenizer_config.json +188 -0
  10. vocab.json +0 -0
README.md ADDED
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+ ---
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+ language:
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+ - en
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+ - es
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+ - fr
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+ - de
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+ - pt
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+ - ja
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+ - it
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+ - zh
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+ - ko
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+ - ar
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+ - cs
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+ - nl
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+ pipeline_tag: text-generation
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+ license: apache-2.0
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+ library_name: transformers
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+ tags:
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+ - granite-3.3
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+ - autoround
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+ - auto-round
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+ - intel-autoround
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+ - intel
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+ - woq
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+ - gptq
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+ - autogptq
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+ - auto-gptq
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+ - pytorch
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+ - ibm
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+ - granite
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+ - granite-3
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+ model_name: Granite 3.3 2b base
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+ base_model:
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+ - ibm-granite/granite-3.3-2b-base
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+ inference: false
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+ model_creator: ibm-granite
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+ prompt_template: '{prompt}'
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+ quantized_by: fbaldassarri
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+ ---
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+
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+ ## Model Information
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+
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+ Quantized version of [ibm-granite/granite-3.3-2b-base](https://huggingface.co/fbaldassarri/ibm-granite/granite-3.3-2b-base) using torch.float32 for quantization tuning.
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+ - 4 bits (INT4)
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+ - group size = 64
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+ - Asymmetrical Quantization
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+ - Method AutoGPTQ
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+
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+ Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.7
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+
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+ Note: this INT4 version of granite-3.3-2b-base has been quantized to run inference through CPU.
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+
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+ ## Replication Recipe
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+
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+ ### Step 1 Install Requirements
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+
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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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+ ```
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+ wget https://github.com/intel/auto-round/archive/refs/tags/v0.4.7.tar.gz
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+ tar -xvzf v0.4.7.tar.gz
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+ cd auto-round-0.4.7
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+ pip install -r requirements-cpu.txt --upgrade
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+ ```
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+
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+ ### Step 2 Build Intel AutoRound wheel from sources
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+
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+ ```
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+ pip install -vvv --no-build-isolation -e .[cpu]
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+ ```
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+
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+ ### Step 3 Script for Quantization
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+
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+ ```
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+ model_name = "ibm-granite/granite-3.3-2b-base"
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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, device = 4, 64, False, 'cpu'
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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, device=device)
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+ autoround.quantize()
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+ output_dir = "./AutoRound/ibm-granite_granite-3.3-2b-base-autogptq-int4-gs64-asym"
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+ autoround.save_quantized(output_dir, format='auto_gptq', inplace=True)
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+ ```
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+
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+ ## License
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+
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+ [Apache 2.0 License](https://choosealicense.com/licenses/apache-2.0/)
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+
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+ ## Disclaimer
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+
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+ This quantized model comes with no warrenty. It has been developed only for research purposes.
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