--- license: apache-2.0 pipeline_tag: text-generation tags: - fp8 - quantized - llm-compressor - compressed-tensors - red hat base_model: - meta-llama/Llama-4-Maverick-17B-128E-Instruct --- # Llama-4-Maverick-17B-128E-Instruct-block-FP8 ## Model Overview - **Model Architecture:** Llama4ForConditionalGeneration - **Input:** Text, Image - **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-4-Maverick-17B-128E-Instruct](https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-Instruct). ### Model Optimizations This model was obtained by quantizing the weights and activations of [meta-llama/Llama-4-Maverick-17B-128E-Instruct](https://huggingface.co/meta-llama/Llama-4-Maverick-17B-128E-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 RedHatAI/Llama-4-Maverick-17B-128E-Instruct-block-FP8 --tensor_parallel_size 8 ``` 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 = "RedHatAI/Llama-4-Maverick-17B-128E-Instruct-block-FP8" messages = [ { "role": "user", "content": [ { "type": "image_url", "image_url": {"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"}, }, {"type": "text", "text": "Describe this image."}, ], } ] 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, AutoModelForImageTextToText from llmcompressor import oneshot from llmcompressor.modeling import replace_modules_for_calibration from llmcompressor.modifiers.quantization import QuantizationModifier from llmcompressor.utils import dispatch_for_generation MODEL_ID = "meta-llama/Llama-4-Maverick-17B-128E-Instruct" # Load model. model = AutoModelForImageTextToText.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 ecipe = QuantizationModifier( targets="Linear", scheme="FP8_BLOCK", ignore=[ "re:.*lm_head", "re:.*self_attn", "re:.*router", "re:.*vision_model.*", "re:.*multi_modal_projector.*", "Llama4TextAttention", ], ) # Apply quantization. oneshot(model=model, recipe=recipe) dispatch_for_generation(model) # 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 OpenLLM leaderboard task, using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). [vLLM](https://docs.vllm.ai/en/stable/) was used for all evaluations.

Evaluation details

**Openllm V1** ``` lm_eval \ --model vllm \ --model_args pretrained="RedHatAI/Llama-4-Maverick-17B-128E-Instruct-block-FP8",dtype=auto,add_bos_token=True,max_model_len=16384,tensor_parallel_size=8,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="RedHatAI/Llama-4-Maverick-17B-128E-Instruct-block-FP8",dtype=auto,add_bos_token=False,max_model_len=16384,tensor_parallel_size=8,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 "RedHatAI/Llama-4-Maverick-17B-128E-Instruct-block-FP8" \ --dataset "humaneval" \ --backend vllm \ --tp 8 \ --greedy evalplus.evaluate --model "RedHatAI/Llama-4-Maverick-17B-128E-Instruct-block-FP8" \ --dataset "mbpp" \ --backend vllm \ --tp 8 \ --greedy ``` **Multimodal Evaluation** ``` lm_eval \ --model vllm-vlm \ --model_args pretrained="RedHatAI/Llama-4-Maverick-17B-128E-Instruct-FP8-block",dtype=auto,add_bos_token=False,max_model_len=1000000,tensor_parallel_size=8,gpu_memory_utilization=0.9,enable_chunked_prefill=True,trust_remote_code=True,max_images=10 \ --tasks mmlu \ --apply_chat_template \ --batch_size auto ```

### Accuracy

Category	Metric	meta-llama/Llama-4-Maverick-17B-128E-Instruct	RedHatAI/Llama-4-Maverick-17B-128E-Instruct-block-FP8	Recovery (%)
OpenLLM V1	ARC-Challenge (Acc-Norm, 25-shot)	73.38	73.38	100.00
	GSM8K (Strict-Match, 5-shot)	93.03	92.72	99.67
	HellaSwag (Acc-Norm, 10-shot)	87.39	87.33	99.93
	MMLU (Acc, 5-shot)	86.03	86.15	100.13
	TruthfulQA (MC2, 0-shot)	62.76	62.90	100.23
	Winogrande (Acc, 5-shot)	79.56	79.40	99.80
	Average Score	80.36	80.31	99.94
OpenLLM V2	IFEval (Inst Level Strict Acc, 0-shot)	89.93	90.89	101.07
	BBH (Acc-Norm, 3-shot)	70.53	71.03	100.71
	Math-Hard (Exact-Match, 4-shot)	64.73	65.26	100.82
	GPQA (Acc-Norm, 0-shot)	31.29	30.54	97.59
	MUSR (Acc-Norm, 0-shot)	46.56	46.03	98.86
	MMLU-Pro (Acc, 5-shot)	64.11	63.95	99.75
	Average Score	61.19	61.28	100.15
Multi-modal	MMMU (val)	79.08	78.50	99.26