| | --- |
| | tags: |
| | - int8 |
| | - vllm |
| | - chat |
| | - neuralmagic |
| | - llmcompressor |
| | language: |
| | - en |
| | - de |
| | - fr |
| | - it |
| | - pt |
| | - hi |
| | - es |
| | - th |
| | pipeline_tag: text-generation |
| | license: llama3.3 |
| | base_model: meta-llama/Llama-3.3-70B-Instruct |
| | --- |
| | <h1 style="display: flex; align-items: center; gap: 10px; margin: 0;"> |
| | Llama-3.3-70B-Instruct-quantized.w8a8 |
| | <img src="https://www.redhat.com/rhdc/managed-files/Catalog-Validated_model_0.png" alt="Model Icon" width="40" style="margin: 0; padding: 0;" /> |
| | </h1> |
| | |
| | <a href="https://www.redhat.com/en/products/ai/validated-models" target="_blank" style="margin: 0; padding: 0;"> |
| | <img src="https://www.redhat.com/rhdc/managed-files/Validated_badge-Dark.png" alt="Validated Badge" width="250" style="margin: 0; padding: 0;" /> |
| | </a> |
| |
|
| | ## Model Overview |
| | - **Model Architecture:** Llama |
| | - **Input:** Text |
| | - **Output:** Text |
| | - **Model Optimizations:** |
| | - **Activation quantization:** INT8 |
| | - **Weight quantization:** INT8 |
| | - **Intended Use Cases:** Intended for commercial and research use multiple languages. Similarly to [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-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). |
| | - **Release Date:** 01/20/2025 |
| | - **Version:** 1.0 |
| | - **Model Developers:** Neural Magic |
| |
|
| | Quantized version of [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-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. |
| | Llama-3.3-70B-Instruct-quantized.w8a8 achieves 99.4% recovery for OpenLLM v1 (using Meta's prompting when available) and 100% for both HumanEval and HumanEval+ pass@1. |
| |
|
| | ### Model Optimizations |
| |
|
| | This model was obtained by quantizing the weights and activations of [Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) to INT8 data type. |
| | This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x). |
| | Weight quantization also reduces disk size requirements by approximately 50%. |
| |
|
| | Only weights and activations of the linear operators within transformers blocks are quantized. |
| | Weights are quantized with a symmetric static per-channel scheme, where a fixed linear scaling factor is applied between INT8 and floating point representations for each output channel dimension. |
| | Activations are quantized with a symmetric dynamic per-token scheme, computing a linear scaling factor at runtime for each token between INT8 and floating point representations. |
| |
|
| | ## 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-ent/Llama-3.3-70B-Instruct-quantized.w8a8" |
| | 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 aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details. |
| |
|
| | <details> |
| | <summary>Deploy on <strong>Red Hat AI Inference Server</strong></summary> |
| | |
| | ```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/Llama-3.3-70B-Instruct-quantized.w8a8 |
| | ``` |
| | See [Red Hat AI Inference Server documentation](https://docs.redhat.com/en/documentation/red_hat_ai_inference_server/) for more details. |
| | </details> |
| |
|
| | <details> |
| | <summary>Deploy on <strong>Red Hat Enterprise Linux AI</strong></summary> |
| | |
| | ```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-3-3-70b-instruct-quantized-w8a8:1.5 |
| | ``` |
| |
|
| | ```bash |
| | # Serve model via ilab |
| | ilab model serve --model-path ~/.cache/instructlab/models/llama-3-3-70b-instruct-quantized-w8a8 |
| | |
| | # Chat with model |
| | ilab model chat --model ~/.cache/instructlab/models/llama-3-3-70b-instruct-quantized-w8a8 |
| | ``` |
| | See [Red Hat Enterprise Linux AI documentation](https://docs.redhat.com/en/documentation/red_hat_enterprise_linux_ai/1.4) for more details. |
| | </details> |
| |
|
| | <details> |
| | <summary>Deploy on <strong>Red Hat Openshift AI</strong></summary> |
| | |
| | ```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-3-3-70b-instruct-quantized-w8a8 # OPTIONAL CHANGE |
| | serving.kserve.io/deploymentMode: RawDeployment |
| | name: llama-3-3-70b-instruct-quantized-w8a8 # 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-3-3-70b-instruct-quantized-w8a8:1.5 |
| | 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 <project-name> |
| | |
| | # 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 <inference-service-name> and <cluster-ingress-domain> below: |
| | # - Run `oc get inferenceservice` to find your URL if unsure. |
| | |
| | # Call the server using curl: |
| | curl https://<inference-service-name>-predictor-default.<domain>/v1/chat/completions |
| | -H "Content-Type: application/json" \ |
| | -d '{ |
| | "model": "llama-3-3-70b-instruct-quantized-w8a8 ", |
| | "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. |
| | </details> |
| |
|
| |
|
| | ## Creation |
| |
|
| | This model was created by using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as presented in the code snipet below. |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForCausalLM |
| | from datasets import Dataset |
| | from llmcompressor.transformers import oneshot |
| | from llmcompressor.modifiers.quantization import GPTQModifier |
| | import random |
| | |
| | model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct" |
| | |
| | num_samples = 1024 |
| | max_seq_len = 8192 |
| | |
| | tokenizer = AutoTokenizer.from_pretrained(model_id) |
| | |
| | max_token_id = len(tokenizer.get_vocab()) - 1 |
| | input_ids = [[random.randint(0, max_token_id) for _ in range(max_seq_len)] for _ in range(num_samples)] |
| | attention_mask = num_samples * [max_seq_len * [1]] |
| | ds = Dataset.from_dict({"input_ids": input_ids, "attention_mask": attention_mask}) |
| | |
| | recipe = GPTQModifier( |
| | targets="Linear", |
| | scheme="W8A8", |
| | ignore=["lm_head"], |
| | dampening_frac=0.01, |
| | ) |
| | |
| | model = SparseAutoModelForCausalLM.from_pretrained( |
| | model_id, |
| | device_map="auto", |
| | ) |
| | |
| | oneshot( |
| | model=model, |
| | dataset=ds, |
| | recipe=recipe, |
| | max_seq_length=max_seq_len, |
| | num_calibration_samples=num_samples, |
| | ) |
| | |
| | model.save_pretrained("Llama-3.3-70B-Instruct-quantized.w8a8") |
| | ``` |
| |
|
| | ## Evaluation |
| |
|
| | This model was evaluated on the well-known 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. |
| |
|
| | 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. |
| |
|
| | ### Accuracy |
| |
|
| | <table> |
| | <tr> |
| | <th>Category |
| | </th> |
| | <th>Benchmark |
| | </th> |
| | <th>Llama-3.3-70B-Instruct |
| | </th> |
| | <th>Llama-3.3-70B-Instruct-quantized.w8a8 (this model) |
| | </th> |
| | <th>Recovery |
| | </th> |
| | </tr> |
| | <tr> |
| | <td rowspan="8" ><strong>OpenLLM v1</strong> |
| | </td> |
| | <td>MMLU (5-shot) |
| | </td> |
| | <td>81.60 |
| | </td> |
| | <td>81.19 |
| | </td> |
| | <td>99.5% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>MMLU (CoT, 0-shot) |
| | </td> |
| | <td>86.58 |
| | </td> |
| | <td>85.92 |
| | </td> |
| | <td>99.2% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>ARC Challenge (0-shot) |
| | </td> |
| | <td>49.23 |
| | </td> |
| | <td>48.04 |
| | </td> |
| | <td>97.6% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>GSM-8K (CoT, 8-shot, strict-match) |
| | </td> |
| | <td>94.16 |
| | </td> |
| | <td>94.01 |
| | </td> |
| | <td>99.8% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>Hellaswag (10-shot) |
| | </td> |
| | <td>86.49 |
| | </td> |
| | <td>86.47 |
| | </td> |
| | <td>100.0% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>Winogrande (5-shot) |
| | </td> |
| | <td>84.77 |
| | </td> |
| | <td>83.74 |
| | </td> |
| | <td>98.8% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>TruthfulQA (0-shot, mc2) |
| | </td> |
| | <td>62.75 |
| | </td> |
| | <td>63.09 |
| | </td> |
| | <td>99.5% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td><strong>Average</strong> |
| | </td> |
| | <td><strong>77.94</strong> |
| | </td> |
| | <td><strong>77.49</strong> |
| | </td> |
| | <td><strong>99.4%</strong> |
| | </td> |
| | </tr> |
| | <tr> |
| | <td rowspan="7" ><strong>OpenLLM v2</strong> |
| | </td> |
| | <td>MMLU-Pro (5-shot) |
| | </td> |
| | <td>51.89 |
| | </td> |
| | <td>51.59 |
| | </td> |
| | <td>99.7% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>IFEval (0-shot) |
| | </td> |
| | <td>90.89 |
| | </td> |
| | <td>90.68 |
| | </td> |
| | <td>99.4% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>BBH (3-shot) |
| | </td> |
| | <td>63.15 |
| | </td> |
| | <td>62.54 |
| | </td> |
| | <td>99.0% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>Math-lvl-5 (4-shot) |
| | </td> |
| | <td>0.17 |
| | </td> |
| | <td>0.00 |
| | </td> |
| | <td>N/A |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>GPQA (0-shot) |
| | </td> |
| | <td>46.10 |
| | </td> |
| | <td>46.44 |
| | </td> |
| | <td>100.8% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>MuSR (0-shot) |
| | </td> |
| | <td>44.35 |
| | </td> |
| | <td>44.34 |
| | </td> |
| | <td>100.0% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td><strong>Average</strong> |
| | </td> |
| | <td><strong>49.42</strong> |
| | </td> |
| | <td><strong>49.27</strong> |
| | </td> |
| | <td><strong>99.7%</strong> |
| | </td> |
| | </tr> |
| | <tr> |
| | <td rowspan="2" ><strong>Coding</strong> |
| | </td> |
| | <td>HumanEval pass@1 |
| | </td> |
| | <td>83.20 |
| | </td> |
| | <td>83.30 |
| | </td> |
| | <td>100.1% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>HumanEval+ pass@1 |
| | </td> |
| | <td>78.40 |
| | </td> |
| | <td>78.60 |
| | </td> |
| | <td>100.3% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td rowspan="9" ><strong>Multilingual</strong> |
| | </td> |
| | <td>Portuguese MMLU (5-shot) |
| | </td> |
| | <td>79.76 |
| | </td> |
| | <td>79.47 |
| | </td> |
| | <td>99.6% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>Spanish MMLU (5-shot) |
| | </td> |
| | <td>79.33 |
| | </td> |
| | <td>79.23 |
| | </td> |
| | <td>99.9% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>Italian MMLU (5-shot) |
| | </td> |
| | <td>79.15 |
| | </td> |
| | <td>78.80 |
| | </td> |
| | <td>99.6% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>German MMLU (5-shot) |
| | </td> |
| | <td>77.94 |
| | </td> |
| | <td>77.92 |
| | </td> |
| | <td>100.0% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>French MMLU (5-shot) |
| | </td> |
| | <td>75.69 |
| | </td> |
| | <td>75.79 |
| | </td> |
| | <td>100.1% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>Hindi MMLU (5-shot) |
| | </td> |
| | <td>73.81 |
| | </td> |
| | <td>73.49 |
| | </td> |
| | <td>99.6% |
| | </td> |
| | </tr> |
| | <tr> |
| | <td>Thai MMLU (5-shot) |
| | </td> |
| | <td>71.97 |
| | </td> |
| | <td>71.44 |
| | </td> |
| | <td>99.2% |
| | </td> |
| | </tr> |
| | </table> |
| |
|
| | ### Reproduction |
| |
|
| | The results were obtained using the following commands: |
| |
|
| | #### MMLU |
| | ``` |
| | lm_eval \ |
| | --model vllm \ |
| | --model_args pretrained="neuralmagic-ent/Llama-3.3-70B-Instruct-quantized.w8a8",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-ent/Llama-3.3-70B-Instruct-quantized.w8a8",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-ent/Llama-3.3-70B-Instruct-quantized.w8a8",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-ent/Llama-3.3-70B-Instruct-quantized.w8a8",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-ent/Llama-3.3-70B-Instruct-quantized.w8a8",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-ent/Llama-3.3-70B-Instruct-quantized.w8a8",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-ent/Llama-3.3-70B-Instruct-quantized.w8a8",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-ent/Llama-3.3-70B-Instruct-quantized.w8a8",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-ent/Llama-3.3-70B-Instruct-quantized.w8a8",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-ent/Llama-3.3-70B-Instruct-quantized.w8a8",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-ent/Llama-3.3-70B-Instruct-quantized.w8a8",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-ent/Llama-3.3-70B-Instruct-quantized.w8a8",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-ent/Llama-3.3-70B-Instruct-quantized.w8a8",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-ent/Llama-3.3-70B-Instruct-quantized.w8a8",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-ent/Llama-3.3-70B-Instruct-quantized.w8a8",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-ent/Llama-3.3-70B-Instruct-quantized.w8a8 \ |
| | --bs 16 \ |
| | --temperature 0.2 \ |
| | --n_samples 50 \ |
| | --root "." \ |
| | --dataset humaneval |
| | ``` |
| | ##### Sanitization |
| | ``` |
| | python3 evalplus/sanitize.py \ |
| | humaneval/neuralmagic-ent--Llama-3.3-70B-Instruct-quantized.w8a8_vllm_temp_0.2 |
| | ``` |
| | ##### Evaluation |
| | ``` |
| | evalplus.evaluate \ |
| | --dataset humaneval \ |
| | --samples humaneval/neuralmagic-ent--Llama-3.3-70B-Instruct-quantized.w8a8_vllm_temp_0.2-sanitized |
| | ``` |
| |
|
