Instructions to use prithivMLmods/LevelField-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/LevelField-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/LevelField-GGUF") 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("prithivMLmods/LevelField-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prithivMLmods/LevelField-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/LevelField-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/LevelField-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/LevelField-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/LevelField-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/LevelField-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/LevelField-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/LevelField-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/LevelField-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/LevelField-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/LevelField-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/LevelField-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/LevelField-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/LevelField-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/LevelField-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/LevelField-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/LevelField-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/LevelField-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/LevelField-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/LevelField-GGUF with Ollama:
ollama run hf.co/prithivMLmods/LevelField-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use prithivMLmods/LevelField-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/LevelField-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/LevelField-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use prithivMLmods/LevelField-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/LevelField-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/LevelField-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/LevelField-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.LevelField-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use prithivMLmods/LevelField-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/LevelField-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/LevelField-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use prithivMLmods/LevelField-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/LevelField-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/LevelField-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"
LevelField-GGUF
LevelField-1 is the first open-source large language model purpose-built for non-standard machining process planning, developed by Level AI (蓝沃AI) and built on Qwen3.5-9B, fine-tuned and reinforced with 100,000 manufacturing drawings and process sheets sourced from 16 factories, with data construction and result validation performed jointly by Level AI's engineering team and 36 process engineers averaging 20+ years of hands-on experience. Given a 2D manufacturing drawing (PDF or PNG), it interprets part geometry, dimensions and tolerances, surface roughness, and other technical specifications, then generates a complete machining process route with optimization suggestions, covering 18 common process types including turning, milling, drilling, grinding, casting, forging, heat treatment, and welding. In internal evaluation, its process reasoning achieved 95%+ alignment with decisions made by senior process engineers, and in pilot customer deployments it reduced process route drafting time by roughly 60%. The 9B-parameter model is released as merged BF16 safetensors weights (~20GB), deployable via vLLM (requiring at least one H100 GPU) with a Qwen3 reasoning parser, or tried directly through Level AI's hosted online demo, and is licensed under Apache 2.0 for free commercial use.
Model Files
| File Name | Quant Type | File Size | File Link |
|---|---|---|---|
| LevelField.BF16.gguf | BF16 | 17.9 GB | Download |
| LevelField.Q3_K_L.gguf | Q3_K_L | 4.93 GB | Download |
| LevelField.Q3_K_M.gguf | Q3_K_M | 4.62 GB | Download |
| LevelField.Q3_K_S.gguf | Q3_K_S | 4.26 GB | Download |
| LevelField.Q4_0.gguf | Q4_0 | 5.31 GB | Download |
| LevelField.Q4_K_M.gguf | Q4_K_M | 5.63 GB | Download |
| LevelField.Q4_K_S.gguf | Q4_K_S | 5.35 GB | Download |
| LevelField.Q5_0.gguf | Q5_0 | 6.31 GB | Download |
| LevelField.Q5_K_M.gguf | Q5_K_M | 6.47 GB | Download |
| LevelField.Q5_K_S.gguf | Q5_K_S | 6.31 GB | Download |
| LevelField.mmproj-bf16.gguf | mmproj-bf16 | 922 MB | Download |
llama.cpp
LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp
- Downloads last month
- 426
3-bit
4-bit
5-bit
16-bit