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--- |
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tags: |
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- fp8 |
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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: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF |
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--- |
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# Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic |
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## Model Overview |
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- **Model Architecture:** Llama-3.1-Nemotron |
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- **Input:** Text |
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- **Output:** Text |
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- **Model Optimizations:** |
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- **Weight quantization:** FP8 |
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- **Activation quantization:** FP8 |
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- **Intended Use Cases:** Intended for commercial and research use in multiple languages. Similarly to [Llama-3.1-Nemotron-70B-Instruct](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF), this model 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:** 10/17/2024 |
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- **Version:** 1.0 |
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- **License(s):** [llama3.1](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B/blob/main/LICENSE) |
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- **Model Developers:** Neural Magic |
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This model is a quantized version of [Llama-3.1-Nemotron-70B-Instruct](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF). |
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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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Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic achieves 99.41% recovery for the Arena-Hard evaluation, 100% for OpenLLM v1 (using Meta's prompting when available), and ToDo for OpenLLM v2. |
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### Model Optimizations |
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This model was obtained by quantizing the weights and activations of [Llama-3.1-Nemotron-70B-Instruct](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct-HF) to FP8 data type, ready for inference with vLLM built from source. |
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This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. |
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Only the weights and activations 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 FP8 representations of the quantized weights and activations. Activations are also quantized on a per-token dynamic basis. |
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## Deployment |
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### Use with vLLM |
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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/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic" |
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number_gpus = 2 |
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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) |
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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 [LLM-Compressor](https://github.com/vllm-project/llm-compressor), as presented in the code snipet below. |
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```python |
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import torch |
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from transformers import AutoTokenizer |
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from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot |
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from llmcompressor.transformers.compression.helpers import ( # noqa |
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calculate_offload_device_map, |
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custom_offload_device_map, |
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) |
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recipe = """ |
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quant_stage: |
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quant_modifiers: |
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QuantizationModifier: |
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ignore: ["lm_head"] |
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config_groups: |
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group_0: |
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weights: |
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num_bits: 8 |
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type: float |
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strategy: channel |
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dynamic: false |
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symmetric: true |
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input_activations: |
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num_bits: 8 |
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type: float |
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strategy: token |
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dynamic: true |
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symmetric: true |
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targets: ["Linear"] |
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""" |
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model_stub = "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF" |
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model_name = model_stub.split("/")[-1] |
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device_map = calculate_offload_device_map( |
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model_stub, reserve_for_hessians=False, num_gpus=1, torch_dtype="auto" |
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) |
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model = SparseAutoModelForCausalLM.from_pretrained( |
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model_stub, torch_dtype="auto", device_map=device_map |
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) |
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output_dir = f"./{model_name}-FP8-dynamic" |
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oneshot( |
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model=model, |
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recipe=recipe, |
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output_dir=output_dir, |
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save_compressed=True, |
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tokenizer=AutoTokenizer.from_pretrained(model_stub), |
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) |
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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, and OpenLLM v2. |
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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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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-3.1-70B-Instruct-evals) and a few fixes to OpenLLM v2 tasks. |
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### Accuracy |
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<table> |
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<tr> |
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<td><strong>Benchmark</strong> |
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</td> |
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<td><strong>nvidia/Llama-3.1-Nemotron-70B-Instruct-HF</strong> |
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</td> |
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<td><strong>neuralmagic/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic </br>(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><strong>Arena Hard</strong> |
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</td> |
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<td><strong>85.0</strong> |
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</td> |
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<td><strong>84.5</strong> |
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</td> |
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<td><strong>99.41%</strong> |
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</td> |
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</tr> |
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<tr> |
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<td><strong>OpenLLM Leaderboard v1</strong> |
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</td> |
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<td><strong>80.1</strong> |
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</td> |
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<td><strong>80.3</strong> |
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</td> |
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<td><strong>100.2%</strong> |
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</td> |
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</tr> |
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<tr> |
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<td><strong>OpenLLM Leaderboard v2</strong> |
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</td> |
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<td><strong>40.2</strong> |
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</td> |
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<td><strong>39.8</strong> |
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</td> |
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<td><strong>99.0%</strong> |
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</td> |
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</tr> |
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</table> |
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<table> |
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<tr> |
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<td><strong>Benchmark (per-task breakdown)</strong> |
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</td> |
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<td><strong>nvidia/Llama-3.1-Nemotron-70B-Instruct-HF</strong> |
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</td> |
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<td><strong>neuralmagic/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic (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><strong>OpenLLM v1</strong> |
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</td> |
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</tr> |
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<tr> |
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<td>MMLU (5-shot) |
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</td> |
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<td>83.51 |
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</td> |
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<td>83.49 |
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</td> |
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<td>99.97% |
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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>85.89 |
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</td> |
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<td>86.18 |
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</td> |
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<td>100.33% |
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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>93.09 |
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</td> |
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<td>93.09 |
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</td> |
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<td>100% |
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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>70.13 |
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</td> |
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<td>69.98 |
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</td> |
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<td>99.78% |
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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>87.39 |
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</td> |
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<td>87.22 |
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</td> |
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<td>99.80% |
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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>84.93 |
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</td> |
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<td>84.93 |
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</td> |
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<td>100% |
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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>55.97 |
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</td> |
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<td>57.12 |
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</td> |
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<td>102.05% |
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</td> |
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</tr> |
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<td><strong>Average</strong> |
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</td> |
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<td><strong>80.1</strong> |
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</td> |
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<td><strong>80.3</strong> |
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</td> |
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<td><strong>100.2%</strong> |
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</td> |
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<tr> |
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<td><strong>OpenLLM v2</strong> |
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</td> |
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</tr> |
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<tr> |
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<td>MMLU-Pro (5-shot) |
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</td> |
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<td>43.45 |
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</td> |
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<td>42.99 |
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</td> |
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<td>98.94% |
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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>73.32 |
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</td> |
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<td>74.08 |
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</td> |
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<td>101.02% |
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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>47.12 |
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</td> |
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<td>46.88 |
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</td> |
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<td>99.5% |
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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>23.85 |
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</td> |
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<td>21.78 |
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</td> |
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<td>91.32% |
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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>13.5 |
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</td> |
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<td>13.35 |
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</td> |
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<td>98.88% |
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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>40.2</strong> |
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</td> |
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<td><strong>39.8</strong> |
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</td> |
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<td><strong>99%</strong> |
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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/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \ |
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--tasks mmlu \ |
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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/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \ |
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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/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \ |
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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/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \ |
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--tasks gsm8k_cot_llama_3.1_instruct \ |
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--apply_chat_template \ |
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--fewshot_as_multiturn \ |
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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/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \ |
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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/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \ |
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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/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=2 \ |
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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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#### 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/Llama-3.1-Nemotron-70B-Instruct-HF-FP8-dynamic",dtype=auto,max_model_len=4096,tensor_parallel_size=2,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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