| | --- |
| | tags: |
| | - int88 |
| | - vllm |
| | language: |
| | - en |
| | - de |
| | - fr |
| | - it |
| | - pt |
| | - hi |
| | - es |
| | - th |
| | pipeline_tag: text-generation |
| | license: llama3.1 |
| | base_model: meta-llama/Meta-Llama-3.1-70B-Instruct |
| | --- |
| | |
| | # Meta-Llama-3.1-70B-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-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-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:** 7/24/2024 |
| | - **Version:** 1.0 |
| | - **License(s):** Llama3.1 |
| | - **Model Developers:** Neural Magic |
| |
|
| | Quantized version of [Meta-Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-Instruct). |
| | It achieves scores within 3.2% 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-3.1-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-70B-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 10% damping factor and 256 sequences taken from Neural Magic's [LLM compression calibration dataset](https://huggingface.co/datasets/neuralmagic/LLM_compression_calibration). |
| |
|
| |
|
| | ## 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-3.1-70B-Instruct-quantized.w8a16" |
| | number_gpus = 4 |
| | 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-3.1-70B-Instruct" |
| | |
| | num_samples = 256 |
| | max_seq_len = 8192 |
| | |
| | tokenizer = AutoTokenizer.from_pretrained(model_id) |
| | |
| | def preprocess_fn(example): |
| | return {"text": tokenizer.apply_chat_template(example["messages"], add_generation_prompt=False, tokenize=False)} |
| | |
| | ds = load_dataset("neuralmagic/LLM_compression_calibration", split="train") |
| | ds = ds.shuffle().select(range(num_samples)) |
| | ds = ds.map(preprocess_fn) |
| | |
| | examples = [tokenizer(example["text"], padding=False, max_length=max_seq_len, truncation=True) for example in ds] |
| | |
| | recipe = GPTQModifier( |
| | targets="Linear", |
| | scheme="W8A16", |
| | ignore=["lm_head"], |
| | dampening_frac=0.1, |
| | ) |
| | |
| | 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-3.1-70B-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-3.1-70B-Instruct-evals). |
| |
|
| | **Note:** Results have been updated after Meta modified the chat template. |
| |
|
| | ### Accuracy |
| |
|
| | #### Open LLM Leaderboard evaluation scores |
| | <table> |
| | <tr> |
| | <td><strong>Benchmark</strong> |
| | </td> |
| | <td><strong>Meta-Llama-3.1-70B-Instruct </strong> |
| | </td> |
| | <td><strong>Meta-Llama-3.1-70B-Instruct-quantized.w8a16 (this model)</strong> |
| | </td> |
| | <td><strong>Recovery</strong> |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>MMLU (5-shot) |
| | </td> |
| | <td>83.94 |
| | </td> |
| | <td>81.37 |
| | </td> |
| | <td>96.9% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>MMLU (CoT, 0-shot) |
| | </td> |
| | <td>86.23 |
| | </td> |
| | <td>83.86 |
| | </td> |
| | <td>97.2% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>ARC Challenge (0-shot) |
| | </td> |
| | <td>93.34 |
| | </td> |
| | <td>92.32 |
| | </td> |
| | <td>98.9% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>GSM-8K (CoT, 8-shot, strict-match) |
| | </td> |
| | <td>95.38 |
| | </td> |
| | <td>92.34 |
| | </td> |
| | <td>96.8% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>Hellaswag (10-shot) |
| | </td> |
| | <td>86.66 |
| | </td> |
| | <td>86.01 |
| | </td> |
| | <td>99.3% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>Winogrande (5-shot) |
| | </td> |
| | <td>85.32 |
| | </td> |
| | <td>85.56 |
| | </td> |
| | <td>100.3% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>TruthfulQA (0-shot, mc2) |
| | </td> |
| | <td>60.65 |
| | </td> |
| | <td>59.39 |
| | </td> |
| | <td>97.9% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td><strong>Average</strong> |
| | </td> |
| | <td><strong>84.50</strong> |
| | </td> |
| | <td><strong>82.98</strong> |
| | </td> |
| | <td><strong>98.2%</strong> |
| | </td> |
| | </tr> |
| | </table> |
| |
|
| | ### Reproduction |
| |
|
| | The results were obtained using the following commands: |
| |
|
| | #### MMLU |
| | ``` |
| | lm_eval \ |
| | --model vllm \ |
| | --model_args pretrained="neuralmagic/Meta-Llama-3.1-70B-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-3.1-70B-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-3.1-70B-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-3.1-70B-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-3.1-70B-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-3.1-70B-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-3.1-70B-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 |
| | ``` |
