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--- |
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tags: |
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- int4 |
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- vllm |
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language: |
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- en |
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- de |
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- fr |
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- it |
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- pt |
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- hi |
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- es |
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- th |
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pipeline_tag: text-generation |
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license: llama3.1 |
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base_model: meta-llama/Meta-Llama-3.1-8B-Instruct |
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--- |
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# Meta-Llama-3.1-8B-Instruct-quantized.w4a16 |
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## Model Overview |
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- **Model Architecture:** Meta-Llama-3 |
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- **Input:** Text |
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- **Output:** Text |
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- **Model Optimizations:** |
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- **Weight quantization:** INT4 |
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- **Intended Use Cases:** Intended for commercial and research use in English. 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. |
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- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. |
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- **Release Date:** 7/26/2024 |
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- **Version:** 1.0 |
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- **License(s):** Llama3.1 |
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- **Model Developers:** Neural Magic |
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This model is a quantized version of [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct). |
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It was evaluated on a several tasks to assess the its quality in comparison to the unquatized model, including multiple-choice, math reasoning, and open-ended text generation. |
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Meta-Llama-3.1-8B-Instruct-quantized.w4a16 achieves 93.0% recovery for the Arena-Hard evaluation, 98.9% for OpenLLM v1 (using Meta's prompting when available), 96.1% for OpenLLM v2, 99.7% for HumanEval pass@1, and 97.4% for HumanEval+ pass@1. |
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### Model Optimizations |
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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 INT4 data type. |
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This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. |
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Only the weights of the linear operators within transformers blocks are quantized. |
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Symmetric per-group quantization is applied, in which a linear scaling per group of 128 parameters maps the INT4 and floating point representations of the quantized weights. |
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[AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) is used for quantization with 10% damping factor and 768 sequences taken from Neural Magic's [LLM compression calibration dataset](https://huggingface.co/datasets/neuralmagic/LLM_compression_calibration). |
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## Deployment |
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This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below. |
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```python |
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from vllm import LLM, SamplingParams |
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from transformers import AutoTokenizer |
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model_id = "neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16" |
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number_gpus = 1 |
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max_model_len = 8192 |
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sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256) |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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messages = [ |
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{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, |
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{"role": "user", "content": "Who are you?"}, |
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] |
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prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) |
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llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=max_model_len) |
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outputs = llm.generate(prompts, sampling_params) |
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generated_text = outputs[0].outputs[0].text |
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print(generated_text) |
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``` |
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vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. |
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## Creation |
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This model was created by applying the [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) library as presented in the code snipet below. |
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Although AutoGPTQ was used for this particular model, Neural Magic is transitioning to using [llm-compressor](https://github.com/vllm-project/llm-compressor) which supports several quantization schemes and models not supported by AutoGPTQ. |
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```python |
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from transformers import AutoTokenizer |
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from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig |
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from datasets import load_dataset |
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model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct" |
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num_samples = 756 |
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max_seq_len = 4064 |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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def preprocess_fn(example): |
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return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)} |
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ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train") |
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ds = ds.shuffle().select(range(num_samples)) |
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ds = ds.map(preprocess_fn) |
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examples = [tokenizer(example["text"], padding=False, max_length=max_seq_len, truncation=True) for example in ds] |
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quantize_config = BaseQuantizeConfig( |
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bits=4, |
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group_size=128, |
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desc_act=True, |
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model_file_base_name="model", |
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damp_percent=0.1, |
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) |
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model = AutoGPTQForCausalLM.from_pretrained( |
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model_id, |
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quantize_config, |
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device_map="auto", |
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) |
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model.quantize(examples) |
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model.save_pretrained("Meta-Llama-3.1-8B-Instruct-quantized.w4a16") |
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``` |
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## Evaluation |
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This model was evaluated on the well-known Arena-Hard, OpenLLM v1, OpenLLM v2, HumanEval, and HumanEval+ benchmarks. |
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In all cases, model outputs were generated with the [vLLM](https://docs.vllm.ai/en/stable/) engine. |
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Arena-Hard evaluations were conducted using the [Arena-Hard-Auto](https://github.com/lmarena/arena-hard-auto) repository. |
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The model generated a single answer for each prompt form Arena-Hard, and each answer was judged twice by GPT-4. |
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We report below the scores obtained in each judgement and the average. |
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OpenLLM v1 and v2 evaluations were conducted using Neural Magic's fork of [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness/tree/llama_3.1_instruct) (branch llama_3.1_instruct). |
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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) and a few fixes to OpenLLM v2 tasks. |
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HumanEval and HumanEval+ evaluations were conducted using Neural Magic's fork of the [EvalPlus](https://github.com/neuralmagic/evalplus) repository. |
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Detailed model outputs are available as HuggingFace datasets for [Arena-Hard](https://huggingface.co/datasets/neuralmagic/quantized-llama-3.1-arena-hard-evals), [OpenLLM v2](https://huggingface.co/datasets/neuralmagic/quantized-llama-3.1-leaderboard-v2-evals), and [HumanEval](https://huggingface.co/datasets/neuralmagic/quantized-llama-3.1-humaneval-evals). |
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**Note:** Results have been updated after Meta modified the chat template. |
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### Accuracy |
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<table> |
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<tr> |
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<td><strong>Category</strong> |
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</td> |
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<td><strong>Benchmark</strong> |
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</td> |
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<td><strong>Meta-Llama-3.1-8B-Instruct </strong> |
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</td> |
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<td><strong>Meta-Llama-3.1-8B-Instruct-quantized.w4a16 (this model)</strong> |
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</td> |
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<td><strong>Recovery</strong> |
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</td> |
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</tr> |
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<tr> |
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<td rowspan="1" ><strong>LLM as a judge</strong> |
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</td> |
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<td>Arena Hard |
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</td> |
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<td>25.8 (25.1 / 26.5) |
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</td> |
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<td>27.2 (27.6 / 26.7) |
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</td> |
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<td>105.4% |
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</td> |
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</tr> |
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<tr> |
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<td rowspan="8" ><strong>OpenLLM v1</strong> |
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</td> |
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<td>MMLU (5-shot) |
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</td> |
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<td>68.3 |
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</td> |
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<td>66.9 |
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</td> |
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<td>97.9% |
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</td> |
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</tr> |
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<tr> |
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<td>MMLU (CoT, 0-shot) |
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</td> |
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<td>72.8 |
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</td> |
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<td>71.1 |
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</td> |
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<td>97.6% |
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</td> |
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</tr> |
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<tr> |
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<td>ARC Challenge (0-shot) |
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</td> |
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<td>81.4 |
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</td> |
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<td>80.2 |
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</td> |
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<td>98.0% |
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</td> |
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</tr> |
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<tr> |
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<td>GSM-8K (CoT, 8-shot, strict-match) |
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</td> |
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<td>82.8 |
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</td> |
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<td>82.9 |
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</td> |
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<td>100.2% |
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</td> |
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</tr> |
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<tr> |
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<td>Hellaswag (10-shot) |
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</td> |
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<td>80.5 |
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</td> |
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<td>79.9 |
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</td> |
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<td>99.3% |
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</td> |
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</tr> |
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<tr> |
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<td>Winogrande (5-shot) |
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</td> |
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<td>78.1 |
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</td> |
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<td>78.0 |
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</td> |
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<td>99.9% |
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</td> |
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</tr> |
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<tr> |
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<td>TruthfulQA (0-shot, mc2) |
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</td> |
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<td>54.5 |
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</td> |
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<td>52.8 |
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</td> |
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<td>96.9% |
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</td> |
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</tr> |
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<tr> |
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<td><strong>Average</strong> |
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</td> |
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<td><strong>74.3</strong> |
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</td> |
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<td><strong>73.5</strong> |
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</td> |
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<td><strong>98.9%</strong> |
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</td> |
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</tr> |
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<tr> |
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<td rowspan="7" ><strong>OpenLLM v2</strong> |
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</td> |
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<td>MMLU-Pro (5-shot) |
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</td> |
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<td>30.8 |
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</td> |
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<td>28.8 |
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</td> |
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<td>93.6% |
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</td> |
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</tr> |
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<tr> |
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<td>IFEval (0-shot) |
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</td> |
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<td>77.9 |
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</td> |
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<td>76.3 |
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</td> |
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<td>98.0% |
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</td> |
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</tr> |
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<tr> |
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<td>BBH (3-shot) |
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</td> |
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<td>30.1 |
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</td> |
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<td>28.9 |
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</td> |
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<td>96.1% |
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</td> |
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</tr> |
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<tr> |
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<td>Math-lvl-5 (4-shot) |
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</td> |
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<td>15.7 |
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</td> |
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<td>14.8 |
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</td> |
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<td>94.4% |
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</td> |
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</tr> |
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<tr> |
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<td>GPQA (0-shot) |
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</td> |
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<td>3.7 |
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</td> |
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<td>4.0 |
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</td> |
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<td>109.8% |
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</td> |
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</tr> |
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<tr> |
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<td>MuSR (0-shot) |
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</td> |
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<td>7.6 |
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</td> |
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<td>6.3 |
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</td> |
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<td>83.2% |
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</td> |
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</tr> |
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<tr> |
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<td><strong>Average</strong> |
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</td> |
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<td><strong>27.6</strong> |
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</td> |
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<td><strong>26.5</strong> |
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</td> |
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<td><strong>96.1%</strong> |
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</td> |
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</tr> |
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<tr> |
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<td rowspan="2" ><strong>Coding</strong> |
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</td> |
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<td>HumanEval pass@1 |
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</td> |
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<td>67.3 |
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</td> |
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<td>67.1 |
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</td> |
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<td>99.7% |
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</td> |
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</tr> |
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<tr> |
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<td>HumanEval+ pass@1 |
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</td> |
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<td>60.7 |
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</td> |
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<td>59.1 |
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</td> |
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<td>97.4% |
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</td> |
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</tr> |
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<tr> |
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<td rowspan="9" ><strong>Multilingual</strong> |
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</td> |
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<td>Portuguese MMLU (5-shot) |
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</td> |
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<td>59.96 |
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</td> |
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<td>58.69 |
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</td> |
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<td>97.9% |
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</td> |
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</tr> |
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<tr> |
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<td>Spanish MMLU (5-shot) |
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</td> |
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<td>60.25 |
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</td> |
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<td>58.39 |
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</td> |
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<td>96.9% |
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</td> |
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</tr> |
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<tr> |
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<td>Italian MMLU (5-shot) |
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</td> |
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<td>59.23 |
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</td> |
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<td>57.82 |
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</td> |
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<td>97.6% |
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</td> |
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</tr> |
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<tr> |
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<td>German MMLU (5-shot) |
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</td> |
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<td>58.63 |
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</td> |
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<td>56.22 |
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</td> |
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<td>95.9% |
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</td> |
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</tr> |
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<tr> |
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<td>French MMLU (5-shot) |
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</td> |
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<td>59.65 |
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</td> |
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<td>57.58 |
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</td> |
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<td>96.5% |
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</td> |
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</tr> |
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<tr> |
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<td>Hindi MMLU (5-shot) |
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</td> |
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<td>50.10 |
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</td> |
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<td>47.14 |
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</td> |
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<td>94.1% |
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</td> |
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</tr> |
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<tr> |
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<td>Thai MMLU (5-shot) |
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</td> |
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<td>49.12 |
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</td> |
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<td>46.72 |
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</td> |
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<td>95.1% |
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</td> |
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</tr> |
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</table> |
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### Reproduction |
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The results were obtained using the following commands: |
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#### MMLU |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ |
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--tasks mmlu_llama_3.1_instruct \ |
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--fewshot_as_multiturn \ |
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--apply_chat_template \ |
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--num_fewshot 5 \ |
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--batch_size auto |
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``` |
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#### MMLU-CoT |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=4064,max_gen_toks=1024,tensor_parallel_size=1 \ |
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--tasks mmlu_cot_0shot_llama_3.1_instruct \ |
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--apply_chat_template \ |
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--num_fewshot 0 \ |
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--batch_size auto |
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``` |
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#### ARC-Challenge |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3940,max_gen_toks=100,tensor_parallel_size=1 \ |
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--tasks arc_challenge_llama_3.1_instruct \ |
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--apply_chat_template \ |
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--num_fewshot 0 \ |
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--batch_size auto |
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``` |
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#### GSM-8K |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=4096,max_gen_toks=1024,tensor_parallel_size=1 \ |
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--tasks gsm8k_cot_llama_3.1_instruct \ |
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--fewshot_as_multiturn \ |
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--apply_chat_template \ |
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--num_fewshot 8 \ |
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--batch_size auto |
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``` |
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#### Hellaswag |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ |
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--tasks hellaswag \ |
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--num_fewshot 10 \ |
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--batch_size auto |
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``` |
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#### Winogrande |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ |
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--tasks winogrande \ |
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--num_fewshot 5 \ |
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--batch_size auto |
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``` |
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#### TruthfulQA |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ |
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--tasks truthfulqa \ |
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--num_fewshot 0 \ |
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--batch_size auto |
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``` |
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|
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#### OpenLLM v2 |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=4096,tensor_parallel_size=1,enable_chunked_prefill=True \ |
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--apply_chat_template \ |
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--fewshot_as_multiturn \ |
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--tasks leaderboard \ |
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--batch_size auto |
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``` |
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#### MMLU Portuguese |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ |
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--tasks mmlu_pt_llama_3.1_instruct \ |
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--fewshot_as_multiturn \ |
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--apply_chat_template \ |
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--num_fewshot 5 \ |
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--batch_size auto |
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``` |
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#### MMLU Spanish |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ |
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--tasks mmlu_es_llama_3.1_instruct \ |
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--fewshot_as_multiturn \ |
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--apply_chat_template \ |
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--num_fewshot 5 \ |
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--batch_size auto |
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``` |
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#### MMLU Italian |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ |
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--tasks mmlu_it_llama_3.1_instruct \ |
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--fewshot_as_multiturn \ |
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--apply_chat_template \ |
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--num_fewshot 5 \ |
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--batch_size auto |
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``` |
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#### MMLU German |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ |
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--tasks mmlu_de_llama_3.1_instruct \ |
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--fewshot_as_multiturn \ |
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--apply_chat_template \ |
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--num_fewshot 5 \ |
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--batch_size auto |
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``` |
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#### MMLU French |
|
``` |
|
lm_eval \ |
|
--model vllm \ |
|
--model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",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.w4a16",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.w4a16",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 |
|
``` |
|
|
|
#### HumanEval and HumanEval+ |
|
##### Generation |
|
``` |
|
python3 codegen/generate.py \ |
|
--model neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16 \ |
|
--bs 16 \ |
|
--temperature 0.2 \ |
|
--n_samples 50 \ |
|
--root "." \ |
|
--dataset humaneval |
|
``` |
|
##### Sanitization |
|
``` |
|
python3 evalplus/sanitize.py \ |
|
humaneval/neuralmagic--Meta-Llama-3.1-8B-Instruct-quantized.w4a16_vllm_temp_0.2 |
|
``` |
|
##### Evaluation |
|
``` |
|
evalplus.evaluate \ |
|
--dataset humaneval \ |
|
--samples humaneval/neuralmagic--Meta-Llama-3.1-8B-Instruct-quantized.w4a16_vllm_temp_0.2-sanitized |
|
``` |
|
|
