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--- |
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tags: |
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- fp8 |
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- vllm |
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license: apache-2.0 |
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license_link: https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md |
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language: |
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- en |
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base_model: ibm-granite/granite-3.1-2b-instruct |
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library_name: transformers |
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--- |
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# granite-3.1-2b-instruct-FP8-dynamic |
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## Model Overview |
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- **Model Architecture:** granite-3.1-2b-instruct |
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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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- **Release Date:** 1/8/2025 |
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- **Version:** 1.0 |
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- **Model Developers:** Neural Magic |
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Quantized version of [ibm-granite/granite-3.1-2b-instruct](https://huggingface.co/ibm-granite/granite-3.1-2b-instruct). |
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It achieves an average score of 61.84 on the [OpenLLM](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) benchmark (version 1), whereas the unquantized model achieves 61.98. |
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### Model Optimizations |
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This model was obtained by quantizing the weights and activations of [ibm-granite/granite-3.1-2b-instruct](https://huggingface.co/ibm-granite/granite-3.1-2b-instruct) to FP8 data type, ready for inference with vLLM >= 0.5.2. |
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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%. Only the weights and activations of the linear operators within transformers blocks are quantized. |
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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 transformers import AutoTokenizer |
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from vllm import LLM, SamplingParams |
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max_model_len, tp_size = 4096, 1 |
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model_name = "neuralmagic/granite-3.1-2b-instruct-FP8-dynamic" |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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llm = LLM(model=model_name, tensor_parallel_size=tp_size, max_model_len=max_model_len, trust_remote_code=True) |
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sampling_params = SamplingParams(temperature=0.3, max_tokens=256, stop_token_ids=[tokenizer.eos_token_id]) |
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messages_list = [ |
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[{"role": "user", "content": "Who are you? Please respond in pirate speak!"}], |
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] |
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prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list] |
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outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params) |
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generated_text = [output.outputs[0].text for output in outputs] |
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print(generated_text) |
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``` |
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vLLM also 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 with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. |
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<details> |
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<summary>Model Creation Code</summary> |
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```bash |
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python quantize.py --model_id ibm-granite/granite-3.1-2b-instruct --save_path "output_dir/" |
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``` |
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```python |
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import argparse |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from llmcompressor.modifiers.quantization import QuantizationModifier |
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from llmcompressor.transformers import oneshot |
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import os |
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def main(): |
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parser = argparse.ArgumentParser(description='Quantize a transformer model to FP8') |
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parser.add_argument('--model_id', type=str, required=True, |
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help='The model ID from HuggingFace (e.g., "meta-llama/Meta-Llama-3-2b-Instruct")') |
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parser.add_argument('--save_path', type=str, default='.', |
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help='Custom path to save the quantized model. If not provided, will use model_name-FP8-dynamic') |
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args = parser.parse_args() |
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# Load model |
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model = AutoModelForCausalLM.from_pretrained( |
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args.model_id, device_map="auto", torch_dtype="auto", trust_remote_code=True, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(args.model_id) |
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# Configure the quantization algorithm and scheme |
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recipe = QuantizationModifier( |
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targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"] |
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) |
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# Apply quantization |
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oneshot(model=model, recipe=recipe) |
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save_path = os.path.join(args.save_path, args.model_id.split("/")[1] + "-FP8-dynamic") |
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os.makedirs(save_path, exist_ok=True) |
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# Save to disk in compressed-tensors format |
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model.save_pretrained(save_path) |
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tokenizer.save_pretrained(save_path) |
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print(f"Model and tokenizer saved to: {save_path}") |
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if __name__ == "__main__": |
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main() |
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``` |
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</details> |
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## Evaluation |
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The model was evaluated on OpenLLM Leaderboard [V1](https://huggingface.co/spaces/open-llm-leaderboard-old/open_llm_leaderboard), OpenLLM Leaderboard [V2](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard#/) and on [HumanEval](https://github.com/neuralmagic/evalplus), using the following commands: |
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<details> |
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<summary>Evaluation Commands</summary> |
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OpenLLM Leaderboard V1: |
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``` |
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lm_eval \ |
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--model vllm \ |
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--model_args pretrained="neuralmagic/granite-3.1-2b-instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \ |
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--tasks openllm \ |
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--write_out \ |
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--batch_size auto \ |
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--output_path output_dir \ |
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--show_config |
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``` |
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OpenLLM Leaderboard 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/granite-3.1-2b-instruct-FP8-dynamic",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \ |
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--tasks leaderboard \ |
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--write_out \ |
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--batch_size auto \ |
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--output_path output_dir \ |
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--show_config |
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``` |
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#### HumanEval |
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##### Generation |
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``` |
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python3 codegen/generate.py \ |
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--model neuralmagic/granite-3.1-2b-instruct-FP8-dynamic \ |
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--bs 16 \ |
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--temperature 0.2 \ |
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--n_samples 50 \ |
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--root "." \ |
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--dataset humaneval |
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``` |
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##### Sanitization |
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``` |
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python3 evalplus/sanitize.py \ |
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humaneval/neuralmagic--granite-3.1-2b-instruct-FP8-dynamic_vllm_temp_0.2 |
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``` |
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##### Evaluation |
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``` |
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evalplus.evaluate \ |
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--dataset humaneval \ |
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--samples humaneval/neuralmagic--granite-3.1-2b-instruct-FP8-dynamic_vllm_temp_0.2-sanitized |
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``` |
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</details> |
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### Accuracy |
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<table> |
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<thead> |
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<tr> |
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<th>Category</th> |
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<th>Metric</th> |
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<th>ibm-granite/granite-3.1-2b-instruct</th> |
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<th>neuralmagic/granite-3.1-2b-instruct-FP8-dynamic</th> |
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<th>Recovery (%)</th> |
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</tr> |
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</thead> |
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<tbody> |
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<!-- OpenLLM V1 --> |
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<tr> |
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<td rowspan="7"><b>OpenLLM V1</b></td> |
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<td>ARC-Challenge (Acc-Norm, 25-shot)</td> |
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<td>55.63</td> |
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<td>55.03</td> |
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<td>98.92</td> |
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</tr> |
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<tr> |
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<td>GSM8K (Strict-Match, 5-shot)</td> |
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<td>60.96</td> |
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<td>61.49</td> |
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<td>100.87</td> |
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</tr> |
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<tr> |
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<td>HellaSwag (Acc-Norm, 10-shot)</td> |
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<td>75.21</td> |
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<td>75.26</td> |
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<td>100.07</td> |
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</tr> |
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<tr> |
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<td>MMLU (Acc, 5-shot)</td> |
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<td>54.38</td> |
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<td>54.24</td> |
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<td>99.74</td> |
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</tr> |
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<tr> |
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<td>TruthfulQA (MC2, 0-shot)</td> |
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<td>55.93</td> |
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<td>55.42</td> |
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<td>99.09</td> |
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</tr> |
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<tr> |
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<td>Winogrande (Acc, 5-shot)</td> |
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<td>69.67</td> |
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<td>69.61</td> |
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<td>99.91</td> |
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</tr> |
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<tr> |
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<td><b>Average Score</b></td> |
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<td><b>61.98</b></td> |
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<td><b>61.84</b></td> |
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<td><b>99.78</b></td> |
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</tr> |
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<!-- OpenLLM Leaderboard V2 --> |
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<tr> |
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<td rowspan="7"><b>OpenLLM V2</b></td> |
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<td>IFEval (Inst Level Strict Acc, 0-shot)</td> |
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<td>67.99</td> |
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<td>66.79</td> |
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<td>98.24</td> |
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</tr> |
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<tr> |
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<td>BBH (Acc-Norm, 3-shot)</td> |
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<td>44.11</td> |
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<td>44.24</td> |
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<td>100.29</td> |
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</tr> |
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<tr> |
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<td>Math-Hard (Exact-Match, 4-shot)</td> |
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<td>8.66</td> |
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<td>7.89</td> |
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<td>91.12</td> |
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</tr> |
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<tr> |
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<td>GPQA (Acc-Norm, 0-shot)</td> |
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<td>28.30</td> |
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<td>26.90</td> |
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<td>95.06</td> |
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</tr> |
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<tr> |
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<td>MUSR (Acc-Norm, 0-shot)</td> |
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<td>35.12</td> |
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<td>35.12</td> |
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<td>100.00</td> |
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</tr> |
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<tr> |
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<td>MMLU-Pro (Acc, 5-shot)</td> |
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<td>26.87</td> |
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<td>28.33</td> |
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<td>105.42</td> |
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</tr> |
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<tr> |
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<td><b>Average Score</b></td> |
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<td><b>35.17</b></td> |
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<td><b>34.88</b></td> |
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<td><b>99.16</b></td> |
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</tr> |
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<!-- HumanEval --> |
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<tr> |
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<td rowspan="2"><b>Coding</b></td> |
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<td>HumanEval Pass@1</td> |
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<td>53.40</td> |
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<td>54.90</td> |
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<td><b>102.81</b></td> |
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</tr> |
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</tbody> |
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</table> |
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## Inference Performance |
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This model achieves up to 1.2x speedup in single-stream deployment on L40 GPUs. |
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The following performance benchmarks were conducted with [vLLM](https://docs.vllm.ai/en/latest/) version 0.6.6.post1, and [GuideLLM](https://github.com/neuralmagic/guidellm). |
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<details> |
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<summary>Benchmarking Command</summary> |
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``` |
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guidellm --model neuralmagic/granite-3.1-2b-instruct-FP8-dynamic --target "http://localhost:8000/v1" --data-type emulated --data "prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>" --max seconds 360 --backend aiohttp_server |
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``` |
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</details> |
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### Single-stream performance (measured with vLLM version 0.6.6.post1) |
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<table> |
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<tr> |
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<td></td> |
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<td></td> |
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<td></td> |
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<th style="text-align: center;" colspan="7" >Latency (s)</th> |
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</tr> |
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<tr> |
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<th>GPU class</th> |
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<th>Model</th> |
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<th>Speedup</th> |
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<th>Code Completion<br>prefill: 256 tokens<br>decode: 1024 tokens</th> |
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<th>Docstring Generation<br>prefill: 768 tokens<br>decode: 128 tokens</th> |
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<th>Code Fixing<br>prefill: 1024 tokens<br>decode: 1024 tokens</th> |
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<th>RAG<br>prefill: 1024 tokens<br>decode: 128 tokens</th> |
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<th>Instruction Following<br>prefill: 256 tokens<br>decode: 128 tokens</th> |
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<th>Multi-turn Chat<br>prefill: 512 tokens<br>decode: 256 tokens</th> |
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<th>Large Summarization<br>prefill: 4096 tokens<br>decode: 512 tokens</th> |
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</tr> |
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<tr> |
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<td style="vertical-align: middle;" rowspan="3" >L40</td> |
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<td>granite-3.1-2b-instruct</td> |
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<td></td> |
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<td>9.3</td> |
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<td>1.2</td> |
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<td>9.4</td> |
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<td>1.2</td> |
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<td>1.2</td> |
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<td>2.3</td> |
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<td>5.0</td> |
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</tr> |
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<tr> |
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<td>granite-3.1-2b-instruct-FP8-dynamic<br>(this model)</td> |
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<td>1.26</td> |
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<td>7.3</td> |
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<td>0.9</td> |
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<td>7.4</td> |
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<td>1.0</td> |
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<td>0.9</td> |
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<td>1.8</td> |
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<td>4.1</td> |
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</tr> |
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<tr> |
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<td>granite-3.1-2b-instruct-quantized.w4a16</td> |
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<td>1.88</td> |
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<td>4.8</td> |
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<td>0.6</td> |
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<td>4.9</td> |
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<td>0.6</td> |
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<td>0.6</td> |
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<td>1.2</td> |
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<td>2.8</td> |
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</tr> |
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</table> |
