--- language: - en - de - es - fr - ja - pt - ar - cs - it - ko - nl - zh base_model: - ibm-granite/granite-3.1-8b-instruct pipeline_tag: text-generation tags: - granite - language - granite-3.1 - conversational - text-generation-inference license: apache-2.0 license_name: apache-2.0 name: RedHatAI/granite-3.1-8b-instruct description: The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including business applications. readme: https://huggingface.co/RedHatAI/granite-3.1-8b-instruct/main/README.md tasks: - text-to-text provider: IBM license_link: https://www.apache.org/licenses/LICENSE-2.0 validated_on: - RHOAI 2.20 - RHAIIS 3.0 - RHELAI 1.5 ---

Granite-3.1-8B-Instruct Model Icon

Validated Badge **Validated on:** RHOAI 2.20, RHAIIS 3.0, RHELAI 1.5 **Model Summary:** Granite-3.1-8B-Instruct is a 8B parameter long-context instruct model finetuned from Granite-3.1-8B-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets tailored for solving long context problems. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. - **Developers:** Granite Team, IBM - **GitHub Repository:** [ibm-granite/granite-3.1-language-models](https://github.com/ibm-granite/granite-3.1-language-models) - **Website**: [Granite Docs](https://www.ibm.com/granite/docs/) - **Paper:** [Granite 3.1 Language Models (coming soon)](https://huggingface.co/collections/ibm-granite/granite-31-language-models-6751dbbf2f3389bec5c6f02d) - **Release Date**: December 18th, 2024 - **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) ## Deployment This model can be deployed efficiently on vLLM, Red Hat Enterprise Linux AI, and Openshift AI, as shown in the example below. Deploy on vLLM ```python from vllm import LLM, SamplingParams from transformers import AutoTokenizer model_id = "RedHatAI/granite-3.1-8b-instruct" number_gpus = 1 sampling_params = SamplingParams(temperature=0.7, top_p=0.8, max_tokens=256) tokenizer = AutoTokenizer.from_pretrained(model_id) prompt = "Give me a short introduction to large language model." llm = LLM(model=model_id, tensor_parallel_size=number_gpus) outputs = llm.generate(prompt, 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 1 \ --max-model-len 32768 \ --enforce-eager --model RedHatAI/granite-3.1-8b-instruct ``` ​​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/granite-3-1-8b-instruct:1.5 ``` ```bash # Serve model via ilab ilab model serve --model-path ~/.cache/instructlab/models/granite-3-1-8b-instruct -- --trust-remote-code # Chat with model ilab model chat --model ~/.cache/instructlab/models/granite-3-1-8b-instruct ``` 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: granite-3-1-8b-instruct # OPTIONAL CHANGE serving.kserve.io/deploymentMode: RawDeployment name: granite-3-1-8b-instruct # 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: args: - '--trust-remote-code' 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-granite-3-1-8b-instruct: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": "granite-3-1-8b-instruct", "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.
**Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.1 models for languages beyond these 12 languages. **Intended Use:** The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including business applications. *Capabilities* * Summarization * Text classification * Text extraction * Question-answering * Retrieval Augmented Generation (RAG) * Code related tasks * Function-calling tasks * Multilingual dialog use cases * Long-context tasks including long document/meeting summarization, long document QA, etc. **Generation:** This is a simple example of how to use Granite-3.1-8B-Instruct model. Install the following libraries: ```shell pip install torch torchvision torchaudio pip install accelerate pip install transformers ``` Then, copy the snippet from the section that is relevant for your use case. ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer device = "auto" model_path = "ibm-granite/granite-3.1-8b-instruct" tokenizer = AutoTokenizer.from_pretrained(model_path) # drop device_map if running on CPU model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device) model.eval() # change input text as desired chat = [ { "role": "user", "content": "Please list one IBM Research laboratory located in the United States. You should only output its name and location." }, ] chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True) # tokenize the text input_tokens = tokenizer(chat, return_tensors="pt").to(device) # generate output tokens output = model.generate(**input_tokens, max_new_tokens=100) # decode output tokens into text output = tokenizer.batch_decode(output) # print output print(output) ``` **Evaluation Results:**
HuggingFace Open LLM Leaderboard V1
Models ARC-Challenge Hellaswag MMLU TruthfulQA Winogrande GSM8K Avg
Granite-3.1-8B-Instruct 62.62 84.48 65.34 66.23 75.37 73.84 71.31
Granite-3.1-2B-Instruct 54.61 75.14 55.31 59.42 67.48 52.76 60.79
Granite-3.1-3B-A800M-Instruct 50.42 73.01 52.19 49.71 64.87 48.97 56.53
Granite-3.1-1B-A400M-Instruct 42.66 65.97 26.13 46.77 62.35 33.88 46.29
HuggingFace Open LLM Leaderboard V2
Models IFEval BBH MATH Lvl 5 GPQA MUSR MMLU-Pro Avg
Granite-3.1-8B-Instruct 72.08 34.09 21.68 8.28 19.01 28.19 30.55
Granite-3.1-2B-Instruct 62.86 21.82 11.33 5.26 4.87 20.21 21.06
Granite-3.1-3B-A800M-Instruct 55.16 16.69 10.35 5.15 2.51 12.75 17.1
Granite-3.1-1B-A400M-Instruct 46.86 6.18 4.08 0 0.78 2.41 10.05
**Model Architecture:** Granite-3.1-8B-Instruct is based on a decoder-only dense transformer architecture. Core components of this architecture are: GQA and RoPE, MLP with SwiGLU, RMSNorm, and shared input/output embeddings.
Model 2B Dense 8B Dense 1B MoE 3B MoE
Embedding size 2048 4096 1024 1536
Number of layers 40 40 24 32
Attention head size 64 128 64 64
Number of attention heads 32 32 16 24
Number of KV heads 8 8 8 8
MLP hidden size 8192 12800 512 512
MLP activation SwiGLU SwiGLU SwiGLU SwiGLU
Number of experts — — 32 40
MoE TopK — — 8 8
Initialization std 0.1 0.1 0.1 0.1
Sequence length 128K 128K 128K 128K
Position embedding RoPE RoPE RoPE RoPE
# Parameters 2.5B 8.1B 1.3B 3.3B
# Active parameters 2.5B 8.1B 400M 800M
# Training tokens 12T 12T 10T 10T
**Training Data:** Overall, our SFT data is largely comprised of three key sources: (1) publicly available datasets with permissive license, (2) internal synthetic data targeting specific capabilities including long-context tasks, and (3) very small amounts of human-curated data. A detailed attribution of datasets can be found in the [Granite 3.0 Technical Report](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/paper.pdf), [Granite 3.1 Technical Report (coming soon)](https://huggingface.co/collections/ibm-granite/granite-31-language-models-6751dbbf2f3389bec5c6f02d), and [Accompanying Author List](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/author-ack.pdf). **Infrastructure:** We train Granite 3.1 Language Models using IBM's super computing cluster, Blue Vela, which is outfitted with NVIDIA H100 GPUs. This cluster provides a scalable and efficient infrastructure for training our models over thousands of GPUs. **Ethical Considerations and Limitations:** Granite 3.1 Instruct Models are primarily finetuned using instruction-response pairs mostly in English, but also multilingual data covering eleven languages. Although this model can handle multilingual dialog use cases, its performance might not be similar to English tasks. In such case, introducing a small number of examples (few-shot) can help the model in generating more accurate outputs. While this model has been aligned by keeping safety in consideration, the model may in some cases produce inaccurate, biased, or unsafe responses to user prompts. So we urge the community to use this model with proper safety testing and tuning tailored for their specific tasks. **Resources** - ⭐️ Learn about the latest updates with Granite: https://www.ibm.com/granite - 📄 Get started with tutorials, best practices, and prompt engineering advice: https://www.ibm.com/granite/docs/ - 💡 Learn about the latest Granite learning resources: https://ibm.biz/granite-learning-resources