Instructions to use yuvit-batra/qwen2.5-coder-7b-cadquery-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yuvit-batra/qwen2.5-coder-7b-cadquery-gguf with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("yuvit-batra/qwen2.5-coder-7b-cadquery-gguf")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

llama-cpp-python

How to use yuvit-batra/qwen2.5-coder-7b-cadquery-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="yuvit-batra/qwen2.5-coder-7b-cadquery-gguf",
	filename="qwen2.5-coder-7b-cadquery-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use yuvit-batra/qwen2.5-coder-7b-cadquery-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 yuvit-batra/qwen2.5-coder-7b-cadquery-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf yuvit-batra/qwen2.5-coder-7b-cadquery-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 yuvit-batra/qwen2.5-coder-7b-cadquery-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf yuvit-batra/qwen2.5-coder-7b-cadquery-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 yuvit-batra/qwen2.5-coder-7b-cadquery-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf yuvit-batra/qwen2.5-coder-7b-cadquery-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 yuvit-batra/qwen2.5-coder-7b-cadquery-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf yuvit-batra/qwen2.5-coder-7b-cadquery-gguf:Q4_K_M

Use Docker

docker model run hf.co/yuvit-batra/qwen2.5-coder-7b-cadquery-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use yuvit-batra/qwen2.5-coder-7b-cadquery-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yuvit-batra/qwen2.5-coder-7b-cadquery-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": "yuvit-batra/qwen2.5-coder-7b-cadquery-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yuvit-batra/qwen2.5-coder-7b-cadquery-gguf:Q4_K_M

Ollama
How to use yuvit-batra/qwen2.5-coder-7b-cadquery-gguf with Ollama:
```
ollama run hf.co/yuvit-batra/qwen2.5-coder-7b-cadquery-gguf:Q4_K_M
```

Unsloth Studio

How to use yuvit-batra/qwen2.5-coder-7b-cadquery-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 yuvit-batra/qwen2.5-coder-7b-cadquery-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 yuvit-batra/qwen2.5-coder-7b-cadquery-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for yuvit-batra/qwen2.5-coder-7b-cadquery-gguf to start chatting

How to use yuvit-batra/qwen2.5-coder-7b-cadquery-gguf with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "yuvit-batra/qwen2.5-coder-7b-cadquery-gguf"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "yuvit-batra/qwen2.5-coder-7b-cadquery-gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use yuvit-batra/qwen2.5-coder-7b-cadquery-gguf with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "yuvit-batra/qwen2.5-coder-7b-cadquery-gguf"

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 yuvit-batra/qwen2.5-coder-7b-cadquery-gguf

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use yuvit-batra/qwen2.5-coder-7b-cadquery-gguf with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "yuvit-batra/qwen2.5-coder-7b-cadquery-gguf"

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 "yuvit-batra/qwen2.5-coder-7b-cadquery-gguf" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use yuvit-batra/qwen2.5-coder-7b-cadquery-gguf with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "yuvit-batra/qwen2.5-coder-7b-cadquery-gguf"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "yuvit-batra/qwen2.5-coder-7b-cadquery-gguf"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "yuvit-batra/qwen2.5-coder-7b-cadquery-gguf",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use yuvit-batra/qwen2.5-coder-7b-cadquery-gguf with Docker Model Runner:
```
docker model run hf.co/yuvit-batra/qwen2.5-coder-7b-cadquery-gguf:Q4_K_M
```

Lemonade

How to use yuvit-batra/qwen2.5-coder-7b-cadquery-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull yuvit-batra/qwen2.5-coder-7b-cadquery-gguf:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-coder-7b-cadquery-gguf-Q4_K_M

List all available models

lemonade list

Qwen2.5-Coder-7B CadQuery (MLX 4-bit)

A fine-tune of Qwen2.5-Coder-7B-Instruct that turns natural-language part descriptions into executable CadQuery (Python) scripts producing valid, dimensionally-correct B-rep solids exportable to STEP/STL.

Trained with LoRA (rank 16, 16 layers) via mlx-lm on an Apple M4 Max; this repo contains the fused 4-bit MLX weights. A GGUF build for llama.cpp/Ollama is published alongside this repo.

Prompt contract

Use this exact system prompt (the model was trained with it):

You are a CAD design assistant. Given a description of a mechanical part, respond with a complete CadQuery (Python) script that builds it. Use millimeters. The script must import cadquery as cq and assign the final single-solid model to a variable named result. Respond with only the code.

Set the stop token to <|im_end|>.

mlx_lm.generate --model yuvit-batra/qwen2.5-coder-7b-cadquery-mlx-4bit \
  --prompt "Design a 60mm diameter flange, 10mm thick, with a 20mm bore and six 6mm bolt holes on a 44mm circle." \
  --max-tokens 1024 --temp 0.2

Training data

12,477 validated text→CadQuery pairs (dataset v1.2):

8,468 synthetic samples across 22 parametric part families (plates, brackets, flanges, gears, shafts, enclosures, turned/chess-piece-like parts, phone stands, trays, hooks, clips, ISO fasteners...). Every sample was executed in a sandbox and verified to produce a valid single solid with a bounding box matching its description.
4,000 real-world human-designed models: DeepCAD construction sequences with Text2CAD annotations (CC-BY-NC-SA-4.0), converted to CadQuery and re-validated.
9 permissively-licensed GitHub CadQuery examples.

Evaluation (geometric, not textual)

Generated code is executed; solids are checked for validity and dimensional accuracy vs reference geometry (sorted-bbox within 10%/axis, volume within 15%).

Metric (n=25 test / n=33 novel)	Base Qwen2.5-Coder-7B	This model
Test: executes	28%	80%
Test: valid single solid	24%	80%
Test: bbox accuracy	33%	45%
Novel prompts: executes	64%	73%
Novel prompts: valid solid	48%	67%

The novel-prompt set includes chess pieces, phone docks, organizers, and other consumer parts phrased unlike the training templates.

Limitations & license

Trained for single-part generation (no assemblies) in millimeters.
~32% of training data (the Text2CAD portion) uses normalized coordinates ("units"); prompts phrased in mm behave best.
Dataset lineage includes CC-BY-NC-SA-4.0 data (Text2CAD annotations), so this model is released under CC-BY-NC-SA-4.0 — non-commercial use only.
Validate generated geometry before manufacturing; the model can produce dimensionally plausible but incorrect parts.

Downloads last month: 210

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for yuvit-batra/qwen2.5-coder-7b-cadquery-gguf

Base model

Qwen/Qwen2.5-7B

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Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

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