From 9596482c98e66e84621d309fac80a0d62f522f03 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sat, 26 Sep 2026 08:45:13 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a16 Source: Original Platform --- .gitattributes | 35 +++ README.md | 431 +++++++++++++++++++++++++++++++ config.json | 70 +++++ configuration.json | 1 + generation_config.json | 12 + model-00001-of-00002.safetensors | 3 + model-00002-of-00002.safetensors | 3 + model.safetensors.index.json | 3 + recipe.yaml | 8 + special_tokens_map.json | 17 ++ tokenizer.json | 3 + tokenizer_config.json | 3 + 12 files changed, 589 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 config.json create mode 100644 configuration.json create mode 100644 generation_config.json create mode 100644 model-00001-of-00002.safetensors create mode 100644 model-00002-of-00002.safetensors create mode 100644 model.safetensors.index.json create mode 100644 recipe.yaml create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..a6344aa --- /dev/null +++ b/.gitattributes @@ -0,0 +1,35 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..cf01db4 --- /dev/null +++ b/README.md @@ -0,0 +1,431 @@ +--- +tags: +- int8 +- vllm +language: +- en +- de +- fr +- it +- pt +- hi +- es +- th +pipeline_tag: text-generation +license: llama3.1 +base_model: meta-llama/Meta-Llama-3.1-8B-Instruct +--- + +# Meta-Llama-3.1-8B-Instruct-quantized.w8a16 + +## Model Overview +- **Model Architecture:** Meta-Llama-3 + - **Input:** Text + - **Output:** Text +- **Model Optimizations:** + - **Weight quantization:** INT8 +- **Intended Use Cases:** Intended for commercial and research use multiple languages. Similarly to [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct), this models is intended for assistant-like chat. +- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). +- **Release Date:** 7/23/2024 +- **Version:** 1.0 +- **License(s):** Llama3.1 +- **Model Developers:** Neural Magic + +Quantized version of [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct). +It achieves scores within 1% of the scores of the unquantized model for MMLU, ARC-Challenge, GSM-8k, Hellaswag, Winogrande and TruthfulQA. + +### Model Optimizations + +This model was obtained by quantizing the weights of [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) to INT8 data type. +This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. + +Only the weights of the linear operators within transformers blocks are quantized. Symmetric per-channel quantization is applied, in which a linear scaling per output dimension maps the INT8 and floating point representations of the quantized weights. +The [GPTQ](https://arxiv.org/abs/2210.17323) algorithm is applied for quantization, as implemented in the [llm-compressor](https://github.com/vllm-project/llm-compressor) library. GPTQ used a 1% damping factor and 256 sequences of 8,192 random tokens. + + +## Deployment + +This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. + +```python +from vllm import LLM, SamplingParams +from transformers import AutoTokenizer + +model_id = "neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16" +number_gpus = 1 +max_model_len = 8192 + +sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256) + +tokenizer = AutoTokenizer.from_pretrained(model_id) + +messages = [ + {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, + {"role": "user", "content": "Who are you?"}, +] + +prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) + +llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len) + +outputs = llm.generate(prompts, sampling_params) + +generated_text = outputs[0].outputs[0].text +print(generated_text) +``` + +vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. + + +## Creation + +This model was created by using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as presented in the code snipet below. + +```python +from transformers import AutoTokenizer +from datasets import Dataset +from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot +from llmcompressor.modifiers.quantization import GPTQModifier +import random + +model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct" + +num_samples = 256 +max_seq_len = 8192 + +tokenizer = AutoTokenizer.from_pretrained(model_id) + +max_token_id = len(tokenizer.get_vocab()) - 1 +input_ids = [[random.randint(0, max_token_id) for _ in range(max_seq_len)] for _ in range(num_samples)] +attention_mask = num_samples * [max_seq_len * [1]] +ds = Dataset.from_dict({"input_ids": input_ids, "attention_mask": attention_mask}) + +recipe = GPTQModifier( + targets="Linear", + scheme="W8A16", + ignore=["lm_head"], + dampening_frac=0.01, +) + +model = SparseAutoModelForCausalLM.from_pretrained( + model_id, + device_map="auto", + trust_remote_code=True, +) + +oneshot( + model=model, + dataset=ds, + recipe=recipe, + max_seq_length=max_seq_len, + num_calibration_samples=num_samples, +) +model.save_pretrained("Meta-Llama-3.1-8B-Instruct-quantized.w8a16") +``` + + + +## Evaluation + +The model was evaluated on MMLU, ARC-Challenge, GSM-8K, Hellaswag, Winogrande and TruthfulQA. +Evaluation was conducted using the Neural Magic fork of [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness/tree/llama_3.1_instruct) (branch llama_3.1_instruct) and the [vLLM](https://docs.vllm.ai/en/stable/) engine. +This version of the lm-evaluation-harness includes versions of MMLU, ARC-Challenge and GSM-8K that match the prompting style of [Meta-Llama-3.1-Instruct-evals](https://huggingface.co/datasets/meta-llama/Meta-Llama-3.1-8B-Instruct-evals). + +**Note:** Results have been updated after Meta modified the chat template. + +### Accuracy + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Category + Benchmark + Meta-Llama-3.1-8B-Instruct + Meta-Llama-3.1-8B-Instruct-quantized.w8a16 (this model) + Recovery +
OpenLLM v1 +
Hellaswag (10-shot) + 80.47 + 80.48 + 100.0% +
Winogrande (5-shot) + 78.06 + 77.51 + 99.3% +
TruthfulQA (0-shot, mc2) + 54.48 + 54.41 + 99.9% +
Average + 74.05 + 74.12 + 100.1% +
Multilingual + Portuguese MMLU (5-shot) + 59.96 + 59.79 + 99.8% +
Spanish MMLU (5-shot) + 60.25 + 59.92 + 99.4% +
Italian MMLU (5-shot) + 59.23 + 59.25 + 100.0% +
German MMLU (5-shot) + 58.63 + 58.31 + 99.5% +
French MMLU (5-shot) + 59.65 + 59.57 + 99.9% +
Hindi MMLU (5-shot) + 50.10 + 49.97 + 99.7% +
Thai MMLU (5-shot) + 49.12 + 49.09 + 99.9% +
+ +### Reproduction + +The results were obtained using the following commands: + +#### MMLU +``` +lm_eval \ + --model vllm \ + --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ + --tasks mmlu_llama_3.1_instruct \ + --fewshot_as_multiturn \ + --apply_chat_template \ + --num_fewshot 5 \ + --batch_size auto +``` + +#### MMLU-CoT +``` +lm_eval \ + --model vllm \ + --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=4064,max_gen_toks=1024,tensor_parallel_size=1 \ + --tasks mmlu_cot_0shot_llama_3.1_instruct \ + --apply_chat_template \ + --num_fewshot 0 \ + --batch_size auto +``` + +#### ARC-Challenge +``` +lm_eval \ + --model vllm \ + --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3940,max_gen_toks=100,tensor_parallel_size=1 \ + --tasks arc_challenge_llama_3.1_instruct \ + --apply_chat_template \ + --num_fewshot 0 \ + --batch_size auto +``` + +#### GSM-8K +``` +lm_eval \ + --model vllm \ + --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=4096,max_gen_toks=1024,tensor_parallel_size=1 \ + --tasks gsm8k_cot_llama_3.1_instruct \ + --fewshot_as_multiturn \ + --apply_chat_template \ + --num_fewshot 8 \ + --batch_size auto +``` + +#### Hellaswag +``` +lm_eval \ + --model vllm \ + --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ + --tasks hellaswag \ + --num_fewshot 10 \ + --batch_size auto +``` + +#### Winogrande +``` +lm_eval \ + --model vllm \ + --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ + --tasks winogrande \ + --num_fewshot 5 \ + --batch_size auto +``` + +#### TruthfulQA +``` +lm_eval \ + --model vllm \ + --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ + --tasks truthfulqa \ + --num_fewshot 0 \ + --batch_size auto +``` + +#### MMLU Portuguese +``` +lm_eval \ + --model vllm \ + --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ + --tasks mmlu_pt_llama_3.1_instruct \ + --fewshot_as_multiturn \ + --apply_chat_template \ + --num_fewshot 5 \ + --batch_size auto +``` + +#### MMLU Spanish +``` +lm_eval \ + --model vllm \ + --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ + --tasks mmlu_es_llama_3.1_instruct \ + --fewshot_as_multiturn \ + --apply_chat_template \ + --num_fewshot 5 \ + --batch_size auto +``` + +#### MMLU Italian +``` +lm_eval \ + --model vllm \ + --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ + --tasks mmlu_it_llama_3.1_instruct \ + --fewshot_as_multiturn \ + --apply_chat_template \ + --num_fewshot 5 \ + --batch_size auto +``` + +#### MMLU German +``` +lm_eval \ + --model vllm \ + --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ + --tasks mmlu_de_llama_3.1_instruct \ + --fewshot_as_multiturn \ + --apply_chat_template \ + --num_fewshot 5 \ + --batch_size auto +``` + +#### MMLU French +``` +lm_eval \ + --model vllm \ + --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ + --tasks mmlu_fr_llama_3.1_instruct \ + --fewshot_as_multiturn \ + --apply_chat_template \ + --num_fewshot 5 \ + --batch_size auto +``` + +#### MMLU Hindi +``` +lm_eval \ + --model vllm \ + --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ + --tasks mmlu_hi_llama_3.1_instruct \ + --fewshot_as_multiturn \ + --apply_chat_template \ + --num_fewshot 5 \ + --batch_size auto +``` + +#### MMLU Thai +``` +lm_eval \ + --model vllm \ + --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ + --tasks mmlu_th_llama_3.1_instruct \ + --fewshot_as_multiturn \ + --apply_chat_template \ + --num_fewshot 5 \ + --batch_size auto +``` \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..ea83f69 --- /dev/null +++ b/config.json @@ -0,0 +1,70 @@ +{ + "_name_or_path": "/network/alexandre/models/meta_llama__Meta-Llama-3.1-8B-Instruct", + "architectures": [ + "LlamaForCausalLM" + ], + "attention_bias": false, + 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