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README.md CHANGED
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- ---
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- license: apache-2.0
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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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+ - olmo
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+ - autoround
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+ - intel-autoround
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+ - auto-awq
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+ - autogawq
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+ - woq
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+ - awq
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+ - gptq
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+ - intel
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+ - pytorch
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+ license: apache-2.0
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+ model_name: OLMo 2 7B
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+ base_model:
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+ - allenai/OLMo-2-1124-7B
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+ inference: false
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+ model_creator: allenai
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+ datasets:
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+ - allenai/RLVR-GSM
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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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+
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+ ## Model Information
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+
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+ Quantized version of [allenai/OLMo-2-1124-7B](https://huggingface.co/allenai/OLMo-2-1124-7B) 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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+
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+ Quantization framework: [Intel AutoRound](https://github.com/intel/auto-round) v0.4.2
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+
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+ Note: this INT4 version of OLMo-2-1124-7B 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/v0.4.2.tar.gz
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+ tar -xvzf v0.4.2.tar.gz
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+ cd auto-round-0.4.2
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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 = "allenai/OLMo-2-1124-7B"
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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, amp = 4, 128, False, 'cpu', 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, device=device, amp=amp)
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+ autoround.quantize()
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+ output_dir = "./AutoRound/allenai_OLMo-2-1124-7B-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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+
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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 warranty. It has been developed only for research purposes.
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+
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+ {
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+ "_name_or_path": "allenai/OLMo-2-1124-7B",
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+ "architectures": [
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+ ],
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+ "attention_dropout": 0.0,
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+ "hidden_act": "silu",
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+ "initializer_range": 0.02,
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+ "intermediate_size": 11008,
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+ "batch_size": 4,
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+ "bits": 4,
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+ "data_type": "int",
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+ "dataset": "NeelNanda/pile-10k",
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+ "enable_minmax_tuning": true,
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+ "enable_norm_bias_tuning": false,
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+ "enable_quanted_input": true,
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+ "gradient_accumulate_steps": 1,
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+ "group_size": 128,
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+ "iters": 200,
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+ "low_gpu_mem_usage": false,
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+ "lr": 0.005,
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+ "minmax_lr": 0.005,
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+ "modules_to_not_convert": [
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+ "lm_head"
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+ ],
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+ "nsamples": 128,
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+ "quant_method": "awq",
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+ "scale_dtype": "torch.float16",
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+ "seqlen": 512,
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+ "sym": false,
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+ "to_quant_block_names": null,
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+ "version": "gemm",
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+ "zero_point": true
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+ },
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+ "rms_norm_eps": 1e-06,
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+ "rope_scaling": null,
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+ "rope_theta": 500000,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.47.0",
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+ "use_cache": true,
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+ "vocab_size": 100352
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+ }
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+ }
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