--- tags: - int4 - vllm language: - en - de - fr - it - pt - hi - es - th pipeline_tag: text-generation license: llama3.1 base_model: meta-llama/Meta-Llama-3.1-8B-Instruct ---

Meta-Llama-3.1-8B-Instruct-quantized.w4a16 Model Icon

Validated Badge ## Model Overview - **Model Architecture:** Meta-Llama-3 - **Input:** Text - **Output:** Text - **Model Optimizations:** - **Weight quantization:** INT4 - **Intended Use Cases:** Intended for commercial and research use in English. Similarly to [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-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). Use in languages other than English. - **Release Date:** 7/26/2024 - **Version:** 1.0 - **License(s):** Llama3.1 - **Model Developers:** Neural Magic This model is a quantized version of [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct). 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. Meta-Llama-3.1-8B-Instruct-quantized.w4a16 achieves 93.0% recovery for the Arena-Hard evaluation, 98.9% for OpenLLM v1 (using Meta's prompting when available), 96.1% for OpenLLM v2, 99.7% for HumanEval pass@1, and 97.4% for HumanEval+ pass@1. ### Model Optimizations This model was obtained by quantizing the weights of [Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights of the linear operators within transformers blocks are quantized. Symmetric per-group quantization is applied, in which a linear scaling per group of 128 parameters maps the INT4 and floating point representations of the quantized weights. [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) is used for quantization with 10% damping factor and 768 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-3.1-8B-Instruct-quantized.w4a16" number_gpus = 1 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 also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
Deploy on Red Hat AI Inference Server ```bash $ podman run --rm -it --device nvidia.com/gpu=all -p 8000:8000 \ --ipc=host \ --env "HUGGING_FACE_HUB_TOKEN=$HF_TOKEN" \ --env "HF_HUB_OFFLINE=0" -v ~/.cache/vllm:/home/vllm/.cache \ --name=vllm \ registry.access.redhat.com/rhaiis/rh-vllm-cuda \ vllm serve \ --tensor-parallel-size 8 \ --max-model-len 32768 \ --enforce-eager --model RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16 ``` ​​See [Red Hat AI Inference Server documentation](https://docs.redhat.com/en/documentation/red_hat_ai_inference_server/) for more details.
Deploy on Red Hat Enterprise Linux AI ```bash # Download model from Red Hat Registry via docker # Note: This downloads the model to ~/.cache/instructlab/models unless --model-dir is specified. ilab model download --repository docker://registry.redhat.io/rhelai1/llama-3-1-8b-instruct-quantized-w4a16:1.5 ``` ```bash # Serve model via ilab ilab model serve --model-path ~/.cache/instructlab/models/llama-3-1-8b-instruct-quantized-w4a16 # Chat with model ilab model chat --model ~/.cache/instructlab/models/llama-3-1-8b-instruct-quantized-w4a16 ``` See [Red Hat Enterprise Linux AI documentation](https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/1.4) for more details.
Deploy on Red Hat Openshift AI ```python # Setting up vllm server with ServingRuntime # Save as: vllm-servingruntime.yaml apiVersion: serving.kserve.io/v1alpha1 kind: ServingRuntime metadata: name: vllm-cuda-runtime # OPTIONAL CHANGE: set a unique name annotations: openshift.io/display-name: vLLM NVIDIA GPU ServingRuntime for KServe opendatahub.io/recommended-accelerators: '["nvidia.com/gpu"]' labels: opendatahub.io/dashboard: 'true' spec: annotations: prometheus.io/port: '8080' prometheus.io/path: '/metrics' multiModel: false supportedModelFormats: - autoSelect: true name: vLLM containers: - name: kserve-container image: quay.io/modh/vllm:rhoai-2.20-cuda # CHANGE if needed. If AMD: quay.io/modh/vllm:rhoai-2.20-rocm command: - python - -m - vllm.entrypoints.openai.api_server args: - "--port=8080" - "--model=/mnt/models" - "--served-model-name={{.Name}}" env: - name: HF_HOME value: /tmp/hf_home ports: - containerPort: 8080 protocol: TCP ``` ```python # Attach model to vllm server. This is an NVIDIA template # Save as: inferenceservice.yaml apiVersion: serving.kserve.io/v1beta1 kind: InferenceService metadata: annotations: openshift.io/display-name: llama-3-1-8b-instruct-quantized-w4a16 # OPTIONAL CHANGE serving.kserve.io/deploymentMode: RawDeployment name: llama-3-1-8b-instruct-quantized-w4a16 # specify model name. This value will be used to invoke the model in the payload labels: opendatahub.io/dashboard: 'true' spec: predictor: maxReplicas: 1 minReplicas: 1 model: modelFormat: name: vLLM name: '' resources: limits: cpu: '2' # this is model specific memory: 8Gi # this is model specific nvidia.com/gpu: '1' # this is accelerator specific requests: # same comment for this block cpu: '1' memory: 4Gi nvidia.com/gpu: '1' runtime: vllm-cuda-runtime # must match the ServingRuntime name above storageUri: oci://registry.redhat.io/rhelai1/modelcar-llama-3-1-8b-instruct-quantized-w4a16:1.5 tolerations: - effect: NoSchedule key: nvidia.com/gpu operator: Exists ``` ```bash # make sure first to be in the project where you want to deploy the model # oc project # apply both resources to run model # Apply the ServingRuntime oc apply -f vllm-servingruntime.yaml # Apply the InferenceService oc apply -f qwen-inferenceservice.yaml ``` ```python # Replace and below: # - Run `oc get inferenceservice` to find your URL if unsure. # Call the server using curl: curl https://-predictor-default./v1/chat/completions -H "Content-Type: application/json" \ -d '{ "model": "llama-3-1-8b-instruct-quantized-w4a16", "stream": true, "stream_options": { "include_usage": true }, "max_tokens": 1, "messages": [ { "role": "user", "content": "How can a bee fly when its wings are so small?" } ] }' ``` See [Red Hat Openshift AI documentation](https://docs.redhat.com/en/documentation/red_hat_openshift_ai/2025) for more details.
## Creation This model was created by applying the [AutoGPTQ](https://github.com/AutoGPTQ/AutoGPTQ) library as presented in the code snipet below. Although AutoGPTQ was used for this particular model, Neural Magic is transitioning to using [llm-compressor](https://github.com/vllm-project/llm-compressor) which supports several quantization schemes and models not supported by AutoGPTQ. ```python from transformers import AutoTokenizer from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig from datasets import load_dataset model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct" num_samples = 756 max_seq_len = 4064 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] quantize_config = BaseQuantizeConfig( bits=4, group_size=128, desc_act=True, model_file_base_name="model", damp_percent=0.1, ) model = AutoGPTQForCausalLM.from_pretrained( model_id, quantize_config, device_map="auto", ) model.quantize(examples) model.save_pretrained("Meta-Llama-3.1-8B-Instruct-quantized.w4a16") ``` ## Evaluation This model was evaluated on the well-known Arena-Hard, OpenLLM v1, OpenLLM v2, HumanEval, and HumanEval+ benchmarks. In all cases, model outputs were generated with the [vLLM](https://docs.vllm.ai/en/stable/) engine. Arena-Hard evaluations were conducted using the [Arena-Hard-Auto](https://github.com/lmarena/arena-hard-auto) repository. The model generated a single answer for each prompt form Arena-Hard, and each answer was judged twice by GPT-4. We report below the scores obtained in each judgement and the average. 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). 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-3.1-8B-Instruct-evals) and a few fixes to OpenLLM v2 tasks. HumanEval and HumanEval+ evaluations were conducted using Neural Magic's fork of the [EvalPlus](https://github.com/neuralmagic/evalplus) repository. Detailed model outputs are available as HuggingFace datasets for [Arena-Hard](https://huggingface.co/datasets/neuralmagic/quantized-llama-3.1-arena-hard-evals), [OpenLLM v2](https://huggingface.co/datasets/neuralmagic/quantized-llama-3.1-leaderboard-v2-evals), and [HumanEval](https://huggingface.co/datasets/neuralmagic/quantized-llama-3.1-humaneval-evals). **Note:** Results have been updated after Meta modified the chat template. ### Accuracy
Category Benchmark Meta-Llama-3.1-8B-Instruct Meta-Llama-3.1-8B-Instruct-quantized.w4a16 (this model) Recovery
LLM as a judge Arena Hard 25.8 (25.1 / 26.5) 27.2 (27.6 / 26.7) 105.4%
OpenLLM v1 MMLU (5-shot) 68.3 66.9 97.9%
MMLU (CoT, 0-shot) 72.8 71.1 97.6%
ARC Challenge (0-shot) 81.4 80.2 98.0%
GSM-8K (CoT, 8-shot, strict-match) 82.8 82.9 100.2%
Hellaswag (10-shot) 80.5 79.9 99.3%
Winogrande (5-shot) 78.1 78.0 99.9%
TruthfulQA (0-shot, mc2) 54.5 52.8 96.9%
Average 74.3 73.5 98.9%
OpenLLM v2 MMLU-Pro (5-shot) 30.8 28.8 93.6%
IFEval (0-shot) 77.9 76.3 98.0%
BBH (3-shot) 30.1 28.9 96.1%
Math-lvl-5 (4-shot) 15.7 14.8 94.4%
GPQA (0-shot) 3.7 4.0 109.8%
MuSR (0-shot) 7.6 6.3 83.2%
Average 27.6 26.5 96.1%
Coding HumanEval pass@1 67.3 67.1 99.7%
HumanEval+ pass@1 60.7 59.1 97.4%
Multilingual Portuguese MMLU (5-shot) 59.96 58.69 97.9%
Spanish MMLU (5-shot) 60.25 58.39 96.9%
Italian MMLU (5-shot) 59.23 57.82 97.6%
German MMLU (5-shot) 58.63 56.22 95.9%
French MMLU (5-shot) 59.65 57.58 96.5%
Hindi MMLU (5-shot) 50.10 47.14 94.1%
Thai MMLU (5-shot) 49.12 46.72 95.1%
### Reproduction The results were obtained using the following commands: #### MMLU ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",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-3.1-8B-Instruct-quantized.w4a16",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-3.1-8B-Instruct-quantized.w4a16",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-3.1-8B-Instruct-quantized.w4a16",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-3.1-8B-Instruct-quantized.w4a16",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-3.1-8B-Instruct-quantized.w4a16",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-3.1-8B-Instruct-quantized.w4a16",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=1 \ --tasks truthfulqa \ --num_fewshot 0 \ --batch_size auto ``` #### OpenLLM v2 ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=4096,tensor_parallel_size=1,enable_chunked_prefill=True \ --apply_chat_template \ --fewshot_as_multiturn \ --tasks leaderboard \ --batch_size auto ``` #### MMLU Portuguese ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ --tasks mmlu_pt_llama_3.1_instruct \ --fewshot_as_multiturn \ --apply_chat_template \ --num_fewshot 5 \ --batch_size auto ``` #### MMLU Spanish ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ --tasks mmlu_es_llama_3.1_instruct \ --fewshot_as_multiturn \ --apply_chat_template \ --num_fewshot 5 \ --batch_size auto ``` #### MMLU Italian ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ --tasks mmlu_it_llama_3.1_instruct \ --fewshot_as_multiturn \ --apply_chat_template \ --num_fewshot 5 \ --batch_size auto ``` #### MMLU German ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ --tasks mmlu_de_llama_3.1_instruct \ --fewshot_as_multiturn \ --apply_chat_template \ --num_fewshot 5 \ --batch_size auto ``` #### MMLU French ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ --tasks mmlu_fr_llama_3.1_instruct \ --fewshot_as_multiturn \ --apply_chat_template \ --num_fewshot 5 \ --batch_size auto ``` #### MMLU Hindi ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ --tasks mmlu_hi_llama_3.1_instruct \ --fewshot_as_multiturn \ --apply_chat_template \ --num_fewshot 5 \ --batch_size auto ``` #### MMLU Thai ``` lm_eval \ --model vllm \ --model_args pretrained="neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16",dtype=auto,max_model_len=3850,max_gen_toks=10,tensor_parallel_size=1 \ --tasks mmlu_th_llama_3.1_instruct \ --fewshot_as_multiturn \ --apply_chat_template \ --num_fewshot 5 \ --batch_size auto ``` #### HumanEval and HumanEval+ ##### Generation ``` python3 codegen/generate.py \ --model neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w4a16 \ --bs 16 \ --temperature 0.2 \ --n_samples 50 \ --root "." \ --dataset humaneval ``` ##### Sanitization ``` python3 evalplus/sanitize.py \ humaneval/neuralmagic--Meta-Llama-3.1-8B-Instruct-quantized.w4a16_vllm_temp_0.2 ``` ##### Evaluation ``` evalplus.evaluate \ --dataset humaneval \ --samples humaneval/neuralmagic--Meta-Llama-3.1-8B-Instruct-quantized.w4a16_vllm_temp_0.2-sanitized ```