--- pipeline_tag: text-generation inference: false license: apache-2.0 library_name: transformers tags: - language - granite-3.1 base_model: - ibm-granite/granite-3.1-1b-a400m-base --- # granite-3.1-1b-a400m-instruct GGUF Models ## Model Generation Details This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`0a5a3b5c`](https://github.com/ggerganov/llama.cpp/commit/0a5a3b5cdfd887cf0f8e09d9ff89dee130cfcdde). --- Click here to get info on choosing the right GGUF model format --- # Granite-3.1-1B-A400M-Instruct **Model Summary:** Granite-3.1-1B-A400M-Instruct is a 8B parameter long-context instruct model finetuned from Granite-3.1-1B-A400M-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) **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-3.1-1B-A400M-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-3.1-1b-a400m-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-3.1-1B-A400M-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 --- # 🚀 If you find these models useful Help me test my **AI-Powered Quantum Network Monitor Assistant** with **quantum-ready security checks**: 👉 [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : [Source Code Quantum Network Monitor](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/GGUFModelBuilder) 💬 **How to test**: Choose an **AI assistant type**: - `TurboLLM` (GPT-4.1-mini) - `HugLLM` (Hugginface Open-source models) - `TestLLM` (Experimental CPU-only) ### **What I’m Testing** I’m pushing the limits of **small open-source models for AI network monitoring**, specifically: - **Function calling** against live network services - **How small can a model go** while still handling: - Automated **Nmap security scans** - **Quantum-readiness checks** - **Network Monitoring tasks** 🟡 **TestLLM** – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space): - ✅ **Zero-configuration setup** - ⏳ 30s load time (slow inference but **no API costs**) . No token limited as the cost is low. - 🔧 **Help wanted!** If you’re into **edge-device AI**, let’s collaborate! ### **Other Assistants** 🟢 **TurboLLM** – Uses **gpt-4.1-mini** : - **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited. - **Create custom cmd processors to run .net code on Quantum Network Monitor Agents** - **Real-time network diagnostics and monitoring** - **Security Audits** - **Penetration testing** (Nmap/Metasploit) 🔵 **HugLLM** – Latest Open-source models: - 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita. ### 💡 **Example commands you could test**: 1. `"Give me info on my websites SSL certificate"` 2. `"Check if my server is using quantum safe encyption for communication"` 3. `"Run a comprehensive security audit on my server"` 4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a [Quantum Network Monitor Agent](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) to run the .net code on. This is a very flexible and powerful feature. Use with caution! ### Final Word I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is [open source](https://github.com/Mungert69). Feel free to use whatever you find helpful. If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) ☕. Your support helps cover service costs and allows me to raise token limits for everyone. I'm also open to job opportunities or sponsorship. Thank you! 😊