Image-Text-to-Text
Transformers
GGUF
multilingual
internvl_chat
feature-extraction
internvl
unsloth
custom_code
conversational
Instructions to use unsloth/InternVL3-2B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/InternVL3-2B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="unsloth/InternVL3-2B-GGUF", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("unsloth/InternVL3-2B-GGUF", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use unsloth/InternVL3-2B-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 unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: llama cli -hf unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL
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 unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./llama-cli -hf unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL
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 unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL # Run inference directly in the terminal: ./build/bin/llama-cli -hf unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL
Use Docker
docker model run hf.co/unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL
- LM Studio
- Jan
- vLLM
How to use unsloth/InternVL3-2B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/InternVL3-2B-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": "unsloth/InternVL3-2B-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/unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL
- SGLang
How to use unsloth/InternVL3-2B-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 "unsloth/InternVL3-2B-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": "unsloth/InternVL3-2B-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 "unsloth/InternVL3-2B-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": "unsloth/InternVL3-2B-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 unsloth/InternVL3-2B-GGUF with Ollama:
ollama run hf.co/unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL
- Unsloth Studio
How to use unsloth/InternVL3-2B-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for unsloth/InternVL3-2B-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for unsloth/InternVL3-2B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for unsloth/InternVL3-2B-GGUF to start chatting
- Pi
How to use unsloth/InternVL3-2B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use unsloth/InternVL3-2B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL
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 "unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL" \ --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"
- Docker Model Runner
How to use unsloth/InternVL3-2B-GGUF with Docker Model Runner:
docker model run hf.co/unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL
- Lemonade
How to use unsloth/InternVL3-2B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL
Run and chat with the model
lemonade run user.InternVL3-2B-GGUF-UD-Q4_K_XL
List all available models
lemonade list
- Hermes Agent
How to use unsloth/InternVL3-2B-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 unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL
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 unsloth/InternVL3-2B-GGUF:UD-Q4_K_XL
Run Hermes
hermes
- Atomic Chat
| { | |
| "_commit_hash": null, | |
| "architectures": [ | |
| "InternVLChatModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_internvl_chat.InternVLChatConfig", | |
| "AutoModel": "modeling_internvl_chat.InternVLChatModel", | |
| "AutoModelForCausalLM": "modeling_internvl_chat.InternVLChatModel" | |
| }, | |
| "downsample_ratio": 0.5, | |
| "dynamic_image_size": true, | |
| "force_image_size": 448, | |
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| "llm_config": { | |
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| "ps_version": "v2", | |
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| "template": "internvl2_5", | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
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| "unsloth_fixed": true, | |
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| "use_llm_lora": 0, | |
| "use_thumbnail": true, | |
| "vision_config": { | |
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| "add_cross_attention": false, | |
| "architectures": [ | |
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| ], | |
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| "auto_map": { | |
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| "1": "LABEL_1" | |
| }, | |
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| "initializer_factor": 0.1, | |
| "initializer_range": 1e-10, | |
| "intermediate_size": 4096, | |
| "is_decoder": false, | |
| "is_encoder_decoder": false, | |
| "label2id": { | |
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| "laux_allreduce": "all_nodes", | |
| "layer_norm_eps": 1e-06, | |
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| "top_k": 50, | |
| "top_p": 1.0, | |
| "torch_dtype": "bfloat16", | |
| "torchscript": false, | |
| "transformers_version": "4.51.3", | |
| "typical_p": 1.0, | |
| "use_bfloat16": true, | |
| "use_flash_attn": true, | |
| "use_moe": false, | |
| "use_residual": true, | |
| "use_rts": false, | |
| "use_weighted_residual": false | |
| } | |
| } |