Instructions to use prawinrk/VCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prawinrk/VCoder with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prawinrk/VCoder")
model = AutoModelForCausalLM.from_pretrained("prawinrk/VCoder")
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 Settings

vLLM

How to use prawinrk/VCoder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/prawinrk/VCoder

SGLang

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

Unsloth Studio

How to use prawinrk/VCoder 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 prawinrk/VCoder 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 prawinrk/VCoder to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prawinrk/VCoder to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="prawinrk/VCoder",
    max_seq_length=2048,
)

Docker Model Runner
How to use prawinrk/VCoder with Docker Model Runner:
```
docker model run hf.co/prawinrk/VCoder
```

prawinrk commited on 15 days ago

Commit

57effce

verified ·

1 Parent(s): 7175f70

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +27 -0

README.md ADDED Viewed

	@@ -0,0 +1,27 @@

+# VCoder
+VCoder is a Python code generation model fine-tuned from Qwen2.5-Coder-3B-Instruct.
+## Base Model
+Qwen2.5-Coder-3B-Instruct
+## Training
+- Framework: Unsloth
+- Dataset: Python Code Instructions 18K
+- Samples Used: 15,000
+- GPU: Tesla T4
+- LoRA Rank: 8
+## Quantization
+Q8_0 GGUF
+## Usage
+Compatible with:
+- Ollama
+- llama.cpp
+- LM Studio
+- Open WebUI