Hugging Face's logo Hugging Face
  • Models
  • Datasets
  • Spaces
  • Buckets new
  • Docs
  • Enterprise
  • Pricing
    • Website
      • Tasks
      • HuggingChat
      • Collections
      • Languages
      • Organizations
    • Community
      • Blog
      • Posts
      • Daily Papers
      • Hardware
      • Learn
      • Discord
      • Forum
      • GitHub
    • Solutions
      • Team & Enterprise
      • Hugging Face PRO
      • Enterprise Support
      • Inference Providers
      • Inference Endpoints
      • Storage Buckets

  • Log In
  • Sign Up

Nanthasit
/
sakthai-vision-7b

Image-to-Text
Transformers
GGUF
English
llava
vision
multimodal
image-captioning
visual-question-answering
vqa
sakthai
house-of-sak
cpu-inference
edge
llama-cpp
ollama
Eval Results
benchmark
Eval Results (legacy)
conversational
Model card Files Files and versions
xet
Community

Instructions to use Nanthasit/sakthai-vision-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use Nanthasit/sakthai-vision-7b with Transformers:

    # Use a pipeline as a high-level helper
    # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5.
    # You must load the model directly (see below) or downgrade to v4.x with:
    # 'pip install "transformers<5.0.0'
    from transformers import pipeline
    
    pipe = pipeline("image-to-text", model="Nanthasit/sakthai-vision-7b")
    messages = [
        {"role": "user", "content": "Who are you?"},
    ]
    pipe(messages)
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("Nanthasit/sakthai-vision-7b", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
  • Local Apps Settings
  • llama.cpp

    How to use Nanthasit/sakthai-vision-7b 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 Nanthasit/sakthai-vision-7b:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf Nanthasit/sakthai-vision-7b:Q4_K_M
    Install from WinGet (Windows)
    winget install llama.cpp
    # Start a local OpenAI-compatible server with a web UI:
    llama serve -hf Nanthasit/sakthai-vision-7b:Q4_K_M
    # Run inference directly in the terminal:
    llama cli -hf Nanthasit/sakthai-vision-7b: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 Nanthasit/sakthai-vision-7b:Q4_K_M
    # Run inference directly in the terminal:
    ./llama-cli -hf Nanthasit/sakthai-vision-7b: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 Nanthasit/sakthai-vision-7b:Q4_K_M
    # Run inference directly in the terminal:
    ./build/bin/llama-cli -hf Nanthasit/sakthai-vision-7b:Q4_K_M
    Use Docker
    docker model run hf.co/Nanthasit/sakthai-vision-7b:Q4_K_M
  • LM Studio
  • Jan
  • Ollama

    How to use Nanthasit/sakthai-vision-7b with Ollama:

    ollama run hf.co/Nanthasit/sakthai-vision-7b:Q4_K_M
  • Unsloth Studio

    How to use Nanthasit/sakthai-vision-7b 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 Nanthasit/sakthai-vision-7b 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 Nanthasit/sakthai-vision-7b to start chatting
    Using HuggingFace Spaces for Unsloth
    # No setup required
    # Open https://huggingface.co/spaces/unsloth/studio in your browser
    # Search for Nanthasit/sakthai-vision-7b to start chatting
  • Atomic Chat new
  • Docker Model Runner

    How to use Nanthasit/sakthai-vision-7b with Docker Model Runner:

    docker model run hf.co/Nanthasit/sakthai-vision-7b:Q4_K_M
  • Lemonade

    How to use Nanthasit/sakthai-vision-7b with Lemonade:

    Pull the model
    # Download Lemonade from https://lemonade-server.ai/
    lemonade pull Nanthasit/sakthai-vision-7b:Q4_K_M
    Run and chat with the model
    lemonade run user.sakthai-vision-7b-Q4_K_M
    List all available models
    lemonade list
sakthai-vision-7b
Ctrl+K
Ctrl+K
  • 2 contributors
History: 94 commits
Nanthasit's picture
Nanthasit
Card improvement: add MMBench 66.2 to model-index (matches Evaluation table + cron eval), add Limitations (7 honest limits) and Citation (LLaVA + SakThai bibtex), refresh ecosystem velocity 32.8 -> 31.5 (2026-07-31 cron eval)
45e416e verified about 2 hours ago
  • .eval_results
    cron: add eval result for sakthai-vision-7b (metadata health check, run 12) about 6 hours ago
  • eval-results
    Upload eval-results/inference-check-20260731-002823.yaml with huggingface_hub about 10 hours ago
  • .gitattributes
    1.71 kB
    Super-squash branch 'main' using huggingface_hub 2 days ago
  • README.md
    13.6 kB
    Card improvement: add MMBench 66.2 to model-index (matches Evaluation table + cron eval), add Limitations (7 honest limits) and Citation (LLaVA + SakThai bibtex), refresh ecosystem velocity 32.8 -> 31.5 (2026-07-31 cron eval) about 2 hours ago
  • inference-check-20260730-232720.yaml
    1.41 kB
    Upload inference-check-20260730-232720.yaml with huggingface_hub about 11 hours ago
  • llava-1.5-7b-hf-q4_k_m.gguf
    4.08 GB
    xet
    Super-squash branch 'main' using huggingface_hub 2 days ago
  • mmproj-model-f16.gguf
    624 MB
    xet
    Super-squash branch 'main' using huggingface_hub 2 days ago