Instructions to use dekthedev/Vintern-1B-v3_5-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use dekthedev/Vintern-1B-v3_5-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="dekthedev/Vintern-1B-v3_5-GGUF", filename="mmproj-vintern-1b-v3_5-f16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use dekthedev/Vintern-1B-v3_5-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf dekthedev/Vintern-1B-v3_5-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf dekthedev/Vintern-1B-v3_5-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf dekthedev/Vintern-1B-v3_5-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf dekthedev/Vintern-1B-v3_5-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 dekthedev/Vintern-1B-v3_5-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf dekthedev/Vintern-1B-v3_5-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 dekthedev/Vintern-1B-v3_5-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf dekthedev/Vintern-1B-v3_5-GGUF:Q4_K_M
Use Docker
docker model run hf.co/dekthedev/Vintern-1B-v3_5-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use dekthedev/Vintern-1B-v3_5-GGUF with Ollama:
ollama run hf.co/dekthedev/Vintern-1B-v3_5-GGUF:Q4_K_M
- Unsloth Studio
How to use dekthedev/Vintern-1B-v3_5-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 dekthedev/Vintern-1B-v3_5-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 dekthedev/Vintern-1B-v3_5-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for dekthedev/Vintern-1B-v3_5-GGUF to start chatting
- Pi
How to use dekthedev/Vintern-1B-v3_5-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf dekthedev/Vintern-1B-v3_5-GGUF:Q4_K_M
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": "dekthedev/Vintern-1B-v3_5-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use dekthedev/Vintern-1B-v3_5-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf dekthedev/Vintern-1B-v3_5-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 dekthedev/Vintern-1B-v3_5-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use dekthedev/Vintern-1B-v3_5-GGUF with Docker Model Runner:
docker model run hf.co/dekthedev/Vintern-1B-v3_5-GGUF:Q4_K_M
- Lemonade
How to use dekthedev/Vintern-1B-v3_5-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dekthedev/Vintern-1B-v3_5-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Vintern-1B-v3_5-GGUF-Q4_K_M
List all available models
lemonade list
Vintern-1B-v3.5 — GGUF
Quantized by @dekthedev.
All quantized GGUF variants of 5CD-AI/Vintern-1B-v3_5. Quantized with llama.cpp for local and edge deployment.
Files
| Variant | File | Size | Quality | Notes |
|---|---|---|---|---|
| Q4_K_M | vintern-1b-v3_5-q4_k_m.gguf |
491 MB | Very good | 4-bit medium — recommended |
| Q5_K_M | vintern-1b-v3_5-q5_k_m.gguf |
522 MB | Excellent | 5-bit, sharper outputs |
| Q8_0 | vintern-1b-v3_5-q8_0.gguf |
675 MB | Near-lossless | 8-bit, closest to full precision |
| mmproj F16 | mmproj-vintern-1b-v3_5-f16.gguf |
620 MB | — | Vision projector, required for image input |
Usage
llama-cpp-python
from llama_cpp import Llama
from llama_cpp.llama_chat_format import Llava16ChatHandler
handler = Llava16ChatHandler(clip_model_path="mmproj-vintern-1b-v3_5-f16.gguf")
llm = Llama(
model_path="vintern-1b-v3_5-q4_k_m.gguf",
chat_handler=handler,
n_ctx=2048,
n_threads=4,
verbose=False,
)
response = llm.create_chat_completion(
messages=[{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": "data:image/jpeg;base64,<BASE64>"}},
{"type": "text", "text": "Mô tả hình ảnh này."},
],
}],
max_tokens=256,
)
print(response["choices"][0]["message"]["content"])
llama-cli
llama-cli \
--model vintern-1b-v3_5-q4_k_m.gguf \
--mmproj mmproj-vintern-1b-v3_5-f16.gguf \
--image your_image.jpg \
--prompt "<|im_start|>user\nMô tả hình ảnh này.<|im_end|>\n<|im_start|>assistant\n" \
--n-predict 256
Model Info
| Base model | 5CD-AI/Vintern-1B-v3_5 |
| Architecture | InternVL2.5-1B |
| Parameters | 0.9B |
| Languages | Vietnamese 🇻🇳, English, Chinese |
| License | Apache 2.0 |
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