Instructions to use prithivMLmods/Intern-Decision-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/Intern-Decision-4B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/Intern-Decision-4B-GGUF")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithivMLmods/Intern-Decision-4B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prithivMLmods/Intern-Decision-4B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/Intern-Decision-4B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/Intern-Decision-4B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/Intern-Decision-4B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/Intern-Decision-4B-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "prithivMLmods/Intern-Decision-4B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/Intern-Decision-4B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "prithivMLmods/Intern-Decision-4B-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/Intern-Decision-4B-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Ollama
How to use prithivMLmods/Intern-Decision-4B-GGUF with Ollama:
ollama run hf.co/prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use prithivMLmods/Intern-Decision-4B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use prithivMLmods/Intern-Decision-4B-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/Intern-Decision-4B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Intern-Decision-4B-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use prithivMLmods/Intern-Decision-4B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use prithivMLmods/Intern-Decision-4B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "prithivMLmods/Intern-Decision-4B-GGUF:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
Intern-Decision-4B-GGUF
Intern-Decision-4B is a multimodal structured decision model fine-tuned from Qwen3.5-4B by the InternLM team. It takes a shared state, a schema of named questions (
choice,score, or yes/nonoul, 1-16 questions with up to 62 options each), and up to eight optional images, and returns a calibrated answer distribution for every question in one forward pass. It never callsgenerate(). Each option is mapped to a single-token symbol, the prompt is rendered with a JSON answer skeleton containing a<decision>placeholder per field, and logits are read just before each placeholder and softmaxed over that field's candidate symbols only. A fitted temperature of about 1.99, trained on 1,728 calibration cases and checked on 1,693 separate validation cases, is then applied. On the card's benchmark table it averages 90.02, edging the Jev baseline at 88.74, and has the best Brier score (0.347 vs Jev's 0.358). Its ECE of 0.065 is better than Jev's 0.095 but not the best, since JevK5 reaches 0.047. The 4B matches Jev's 73.87 on Jevbench-Hard only in the sense that JevK5 also scores that. Jev itself scores 72.07, so the 4B is ahead there. It is weaker on WildJailBreak (89.86 vs 96.29). A separate 96-case known-distribution pilot shows calibration improving Brier/ECE from 0.628/0.213 to 0.550/0.089, against 0.595/0.130 for Jev, so after calibration it has lower ECE but a worse Brier score than Jev. On an RTX 4090 it averages about 44 ms per query versus about 110 ms for Jev. It runs through a localDecisionEngineHugging Face wrapper whose response follows the Jev envelope, and 0.8B and 2B siblings exist. It is released under Apache 2.0 with the Qwen license preserved.
Model Files
| File Name | Quant Type | File Size | File Link | Description |
|---|---|---|---|---|
| Intern-Decision-4B.BF16.gguf | BF16 | 8.42 GB | Link | Full BF16 weights. Highest quality, largest file size. |
| Intern-Decision-4B.Q3_K_L.gguf | Q3_K_L | 2.42 GB | Link | Lower quality but usable, good for low RAM availability. |
| Intern-Decision-4B.Q3_K_M.gguf | Q3_K_M | 2.26 GB | Link | Low quality. |
| Intern-Decision-4B.Q4_K_M.gguf | Q4_K_M | 2.71 GB | Link | Good quality, default size for most use cases, recommended. |
| Intern-Decision-4B.Q4_K_S.gguf | Q4_K_S | 2.56 GB | Link | Slightly lower quality with more space savings, recommended. |
| Intern-Decision-4B.Q5_K_M.gguf | Q5_K_M | 3.07 GB | Link | High quality, recommended. |
| Intern-Decision-4B.Q5_K_S.gguf | Q5_K_S | 2.99 GB | Link | High quality, recommended. |
| Intern-Decision-4B.Q6_K.gguf | Q6_K | 3.46 GB | Link | Very high quality, near perfect, recommended. |
| Intern-Decision-4B.Q8_0.gguf | Q8_0 | 4.48 GB | Link | Extremely high quality, generally unneeded but max available quant. |
| Intern-Decision-4B.mmproj-bf16.gguf | mmproj-bf16 | 676 MB | Link | Multimodal projection file in BF16 format. Used for vision/language models. |
llama.cpp
LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp
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