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

Libraries

How to use varuneshv/VCoder with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("varuneshv/VCoder")
model = AutoModelForMultimodalLM.from_pretrained("varuneshv/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 varuneshv/VCoder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "varuneshv/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": "varuneshv/VCoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/varuneshv/VCoder

SGLang

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

Unsloth Studio

How to use varuneshv/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 varuneshv/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 varuneshv/VCoder to start chatting

Using HuggingFace Spaces for Unsloth

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

Load model with FastModel

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

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

VCoder / Run.rtf

varuneshv

Upload Run.rtf

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	{\rtf1\ansi\ansicpg1252\cocoartf2867
	\cocoatextscaling0\cocoaplatform0{\fonttbl\f0\fswiss\fcharset0 Helvetica;}
	{\colortbl;\red255\green255\blue255;}
	{\*\expandedcolortbl;;}
	\paperw11900\paperh16840\margl1440\margr1440\vieww11520\viewh8400\viewkind0
	\pard\tx720\tx1440\tx2160\tx2880\tx3600\tx4320\tx5040\tx5760\tx6480\tx7200\tx7920\tx8640\pardirnatural\partightenfactor0

	\f0\fs24 \cf0 Step1:\
	!pip install -U transformers\
	\
	step2:\
	from transformers import pipeline\
	\
	pipe = pipeline("text-generation", model="varuneshv/VCoder")\
	messages = [\
	\{"role": "user", "content": "Who are you?"\},\
	]\
	pipe(messages)\
	\
	step3:\
	\
	from transformers import AutoTokenizer, AutoModelForCausalLM\
	\
	tokenizer = AutoTokenizer.from_pretrained("varuneshv/VCoder")\
	\
	model = AutoModelForCausalLM.from_pretrained(\
	"varuneshv/VCoder"\
	)\
	\
	step4:\
	\
	inputs = tokenizer(\
	"write a python code to merge 3 arrays",\
	return_tensors="pt"\
	)\
	\
	outputs = model.generate(\
	**inputs,\
	max_new_tokens=200\
	)\
	\
	print(tokenizer.decode(outputs[0], skip_special_tokens=True))\
	}