--- license: apache-2.0 pipeline_tag: text-generation tags: - fp8 - quantized - llm-compressor - compressed-tensors - red hat base_model: - meta-llama/Llama-3.3-70B-Instruct --- # Llama-3.3-70B-Instruct-FP8-block ## Model Overview - **Model Architecture:** LlamaForCausalLM - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** FP8 - **Activation quantization:** FP8 - **Release Date:** - **Version:** 1.0 - **Model Developers:**: Red Hat Quantized version of [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct). ### Model Optimizations This model was obtained by quantizing the weights and activations of [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) to FP8 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 and activations of the linear operators within transformers blocks of the language model are quantized. ## Deployment ### Use with vLLM 1. Initialize vLLM server: ``` vllm serve nm-testing/Llama-3.3-70B-Instruct-FP8-block --tensor_parallel_size 4 ``` 2. Send requests to the server: ```python from openai import OpenAI # Modify OpenAI's API key and API base to use vLLM's API server. openai_api_key = "EMPTY" openai_api_base = "http://:8000/v1" client = OpenAI( api_key=openai_api_key, base_url=openai_api_base, ) model = "nm-testing/Llama-3.3-70B-Instruct-FP8-block" messages = [ {"role": "user", "content": "Explain quantum mechanics clearly and concisely."}, ] outputs = client.chat.completions.create( model=model, messages=messages, ) generated_text = outputs.choices[0].message.content print(generated_text) ``` ## Creation This model was quantized using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as shown below.

Creation details

```python from transformers import AutoProcessor, LlamaForCausalLM from llmcompressor import oneshot from llmcompressor.modeling import replace_modules_for_calibration from llmcompressor.modifiers.quantization import QuantizationModifier MODEL_ID = "meta-llama/Llama-3.3-70B-Instruct" # Load model. model = LlamaForCausalLM.from_pretrained(MODEL_ID, dtype="auto") processor = AutoProcessor.from_pretrained(MODEL_ID) model = replace_modules_for_calibration(model) # Configure the quantization algorithm and scheme. # In this case, we: # * quantize the weights to fp8 with per-block quantization # * quantize the activations to fp8 with dynamic token activations recipe = QuantizationModifier( targets="Linear", scheme="FP8_BLOCK", ignore=["lm_head"], ) # Apply quantization. oneshot(model=model, recipe=recipe) # Save to disk in compressed-tensors format. SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-FP8-block" model.save_pretrained(SAVE_DIR) processor.save_pretrained(SAVE_DIR) ```

## Evaluation The model was evaluated on the OpenLLMv1 leaderboard task, using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness), on reasoning tasks using [lighteval](https://github.com/huggingface/lighteval). [vLLM](https://docs.vllm.ai/en/stable/) was used for all evaluations.

Evaluation details

**Openllm V1** ``` lm_eval \ --model vllm \ --model_args pretrained="nm-testing/Llama-3.3-70B-Instruct-FP8-block",dtype=auto,add_bos_token=True,max_model_len=16384,tensor_parallel_size=4,gpu_memory_utilization=0.9,enable_chunked_prefill=True,trust_remote_code=True \ --tasks openllm \ --write_out \ --batch_size auto \ --show_config ``` **Openllm V2** ``` lm_eval \ --model vllm \ --model_args pretrained="nm-testing/Llama-3.3-70B-Instruct-FP8-block",dtype=auto,add_bos_token=False,max_model_len=16384,tensor_parallel_size=4,gpu_memory_utilization=0.7,disable_log_stats=True,enable_chunked_prefill=True,trust_remote_code=True \ --tasks leaderboard \ --apply_chat_template \ --fewshot_as_multiturn \ --write_out \ --batch_size auto \ --show_config ``` **Coding Benchmarks** ``` evalplus.evaluate --model "nm-testing/Llama-3.3-70B-Instruct-FP8-block" \ --dataset "humaneval" \ --backend vllm \ --tp 4 \ --greedy evalplus.evaluate --model "nm-testing/Llama-3.3-70B-Instruct-FP8-block" \ --dataset "mbpp" \ --backend vllm \ --tp 4 \ --greedy ```

### Accuracy

Category	Metric	meta-llama/Llama-3.3-70B-Instruct	nm-testing/Llama-3.3-70B-Instruct-FP8-block	Recovery (%)
OpenLLM V1	ARC-Challenge (Acc-Norm, 25-shot)	72.53	72.61	100.12
	GSM8K (Strict-Match, 5-shot)	76.35	73.16	95.83
	HellaSwag (Acc-Norm, 10-shot)	86.65	86.56	99.90
	MMLU (Acc, 5-shot)	82.51	82.38	99.84
	TruthfulQA (MC2, 0-shot)	62.83	62.64	99.69
	Winogrande (Acc, 5-shot)	83.50	83.27	99.72
	Average Score	77.39	76.77	99.20
OpenLLM V2	IFEval (Inst Level Strict Acc, 0-shot)	92.57	92.57	100.00
	BBH (Acc-Norm, 3-shot)	69.03	68.98	99.92
	Math-Hard (Exact-Match, 4-shot)	49.24	49.47	100.46
	GPQA (Acc-Norm, 0-shot)	32.63	32.63	100.00
	MUSR (Acc-Norm, 0-shot)	44.31	43.92	99.10
	MMLU-Pro (Acc, 5-shot)	53.55	53.56	100.02
	Average Score	56.89	56.85	99.93
Coding	HumanEval pass@1	82.90	82.90	100.00
	HumanEval+ pass@1	78.70	77.40	98.35
	MBPP pass@1	87.70	87.60	99.89
	MBPP+ pass@1	72.80	73.00	100.27