Instructions to use unit-mesh/autodev-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unit-mesh/autodev-coder with Transformers:

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

pipe = pipeline("text-generation", model="unit-mesh/autodev-coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("unit-mesh/autodev-coder")
model = AutoModelForCausalLM.from_pretrained("unit-mesh/autodev-coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use unit-mesh/autodev-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "unit-mesh/autodev-coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "unit-mesh/autodev-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/unit-mesh/autodev-coder

SGLang

How to use unit-mesh/autodev-coder 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 "unit-mesh/autodev-coder" \
    --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": "unit-mesh/autodev-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "unit-mesh/autodev-coder" \
        --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": "unit-mesh/autodev-coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use unit-mesh/autodev-coder with Docker Model Runner:
```
docker model run hf.co/unit-mesh/autodev-coder
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

1. Introduction of AutoDev Coder

AutoDev Coder based on deepseek-coder-6.7b-instruct with https://huggingface.co/datasets/unit-mesh/autodev-datasets

deepseek-coder-6.7b-instruct is a 6.7B parameter model initialized from deepseek-coder-6.7b-base and fine-tuned on 2B tokens of instruction data.

Home Page: DeepSeek
Repository: deepseek-ai/deepseek-coder
Chat With DeepSeek Coder: DeepSeek-Coder

2. License

This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.

See the LICENSE-MODEL for more details.

Downloads last month: 16

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for unit-mesh/autodev-coder

Quantizations

2 models

unit-mesh
/

autodev-coder

1. Introduction of AutoDev Coder

2. License

Model tree for unit-mesh/autodev-coder

Spaces using unit-mesh/autodev-coder 2