Instructions to use distil-labs/Distil-Localdoc-Qwen3-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use distil-labs/Distil-Localdoc-Qwen3-0.6B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="distil-labs/Distil-Localdoc-Qwen3-0.6B")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("distil-labs/Distil-Localdoc-Qwen3-0.6B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use distil-labs/Distil-Localdoc-Qwen3-0.6B 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 distil-labs/Distil-Localdoc-Qwen3-0.6B
# Run inference directly in the terminal:
llama cli -hf distil-labs/Distil-Localdoc-Qwen3-0.6B

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf distil-labs/Distil-Localdoc-Qwen3-0.6B
# Run inference directly in the terminal:
llama cli -hf distil-labs/Distil-Localdoc-Qwen3-0.6B

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 distil-labs/Distil-Localdoc-Qwen3-0.6B
# Run inference directly in the terminal:
./llama-cli -hf distil-labs/Distil-Localdoc-Qwen3-0.6B

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 distil-labs/Distil-Localdoc-Qwen3-0.6B
# Run inference directly in the terminal:
./build/bin/llama-cli -hf distil-labs/Distil-Localdoc-Qwen3-0.6B

Use Docker

docker model run hf.co/distil-labs/Distil-Localdoc-Qwen3-0.6B

LM Studio
Jan

vLLM

How to use distil-labs/Distil-Localdoc-Qwen3-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "distil-labs/Distil-Localdoc-Qwen3-0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "distil-labs/Distil-Localdoc-Qwen3-0.6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/distil-labs/Distil-Localdoc-Qwen3-0.6B

SGLang

How to use distil-labs/Distil-Localdoc-Qwen3-0.6B 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 "distil-labs/Distil-Localdoc-Qwen3-0.6B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "distil-labs/Distil-Localdoc-Qwen3-0.6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "distil-labs/Distil-Localdoc-Qwen3-0.6B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "distil-labs/Distil-Localdoc-Qwen3-0.6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use distil-labs/Distil-Localdoc-Qwen3-0.6B with Ollama:
```
ollama run hf.co/distil-labs/Distil-Localdoc-Qwen3-0.6B
```
Unsloth Desktop
Docker Model Runner
How to use distil-labs/Distil-Localdoc-Qwen3-0.6B with Docker Model Runner:
```
docker model run hf.co/distil-labs/Distil-Localdoc-Qwen3-0.6B
```

Lemonade

How to use distil-labs/Distil-Localdoc-Qwen3-0.6B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull distil-labs/Distil-Localdoc-Qwen3-0.6B

Run and chat with the model

lemonade run user.Distil-Localdoc-Qwen3-0.6B-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

distillabs commited on Nov 14, 2025

Commit

3bb7df7

verified ·

1 Parent(s): 7bcba47

Update README.md

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README.md +33 -1

README.md CHANGED Viewed

@@ -1,3 +1,35 @@
 # Distil-Localdoc-Qwen3-0.6B
 A small language model (SLM) fine-tuned by Distil Labs for generating high-quality Python docstrings in Google style. Optimized to run locally via Ollama, ensuring your proprietary code never leaves your infrastructure.
@@ -8,7 +40,7 @@ A small language model (SLM) fine-tuned by Distil Labs for generating high-quali
 - **Developed by**: Distil Labs GmbH
 - **License**: Apache 2.0
-- **Finetuned from**: Qwen/Qwen2.5-0.5B-Instruct
 - **Model Size**: 0.6B parameters
 - **Deployment**: Local inference via Ollama

+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+tags:
+- code
+- python
+- docstring
+- documentation
+- code-generation
+- local-llm
+- privacy
+- ollama
+- qwen3
+- knowledge-distillation
+- developer-tools
+base_model: Qwen/Qwen3-0.6B
+pipeline_tag: text-generation
+model-index:
+- name: Distil-Localdoc-Qwen3-0.6B
+  results:
+  - task:
+      type: text-generation
+      name: Docstring Generation
+    metrics:
+    - type: accuracy
+      value: 0.76
+      name: LLM-as-Judge Accuracy
+      verified: false
+---
 # Distil-Localdoc-Qwen3-0.6B
 A small language model (SLM) fine-tuned by Distil Labs for generating high-quality Python docstrings in Google style. Optimized to run locally via Ollama, ensuring your proprietary code never leaves your infrastructure.
 - **Developed by**: Distil Labs GmbH
 - **License**: Apache 2.0
+- **Finetuned from**: Qwen/Qwen3-0.6B
 - **Model Size**: 0.6B parameters
 - **Deployment**: Local inference via Ollama