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, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("varuneshv/VCoder")
model = AutoModelForCausalLM.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
```

varuneshv commited on 15 days ago

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@@ -1,210 +1,184 @@
 ---
-base_model: unsloth/qwen2.5-coder-3b-instruct-bnb-4bit
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/qwen2.5-coder-3b-instruct-bnb-4bit
-- lora
-- sft
-- transformers
-- trl
 - unsloth
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-## More Information [optional]
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-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.19.1

 ---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-3B-Instruct
 tags:
+- code-generation
+- python
+- qwen
 - unsloth
+- lora
+- gguf
+- coding-assistant
+language:
+- en
+pipeline_tag: text-generation
 ---
+# VCoder
+VCoder is a Python-focused coding assistant fine-tuned from Qwen2.5-Coder-3B-Instruct using LoRA and Unsloth.
+The model was trained on 15,000 Python instruction-response examples from the Python Code Instructions 15K dataset and optimized for Python code generation, problem solving, debugging, and algorithm implementation.
 ## Model Details
+| Attribute | Value |
+|------------|---------|
+| Base Model | Qwen2.5-Coder-3B-Instruct |
+| Fine-Tuning Method | LoRA |
+| Framework | Unsloth |
+| Dataset | Python Code Instructions 15K |
+| Training Samples | 15,000 |
+| GPU | NVIDIA Tesla T4 |
+| Quantized Format | GGUF Q8_0 |
+| Primary Language | Python |
+---
+## Training Pipeline
+Training was performed incrementally:
+| Stage | Samples |
+|---------|---------|
+| Stage 1 | 0 - 5,000 |
+| Stage 2 | 5,000 - 10,000 |
+| Stage 3 | 10,000 - 15,000 |
+The model was trained using parameter-efficient fine-tuning (LoRA), allowing adaptation of the base model while keeping computational requirements low.
+---
+## Benchmark Results
+### HumanEval Comparison
+The model was evaluated against the original Qwen2.5-Coder-3B-Instruct on HumanEval coding tasks.
+| Model | Pass@1 |
+|---------|---------|
+| Base Qwen2.5-Coder-3B | 61.0% |
+| VCoder | 68.0% |
+### Improvement
+```text
++7.0% Pass@1 improvement
+```
+This demonstrates that the fine-tuned model performs better on Python coding tasks than the original base model.
+---
+## Example Usage
+### Python
+```python
+prompt = """
+### Instruction:
+Write a Python function to reverse a string.
+### Input:
+### Response:
+"""
+```
+### Example Output
+```python
+def reverse_string(text):
+    return text[::-1]
+```
+---
+## Supported Tasks
+- Python Code Generation
+- Algorithm Design
+- Data Structures
+- Debugging
+- Code Refactoring
+- Coding Interview Questions
+- Competitive Programming
+- Function Completion
+---
+## GGUF Usage
+Compatible with:
+- Ollama
+- LM Studio
+- llama.cpp
+- Open WebUI
+- Jan
+### Ollama
+Create a Modelfile:
+```text
+FROM ./VCoder.Q8_0.gguf
+```
+Build:
+```bash
+ollama create vcoder -f Modelfile
+```
+Run:
+```bash
+ollama run vcoder
+```
+---
+## Training Dataset
+Dataset used:
+Python Code Instructions 15K
+The dataset contains instruction-response pairs focused on Python programming tasks including:
+- Function generation
+- Data manipulation
+- Algorithms
+- Debugging
+- Problem solving
+---
+## Limitations
+- Primarily optimized for Python.
+- Benchmark performed on a subset of HumanEval tasks.
+- May generate incorrect code for highly specialized domains.
+- Should not be used as the sole source of production-critical code.
+---
+## Acknowledgements
+- Qwen Team for Qwen2.5-Coder
+- Unsloth for efficient fine-tuning
+- Hugging Face
+- OpenAI HumanEval Benchmark
+---
+## Citation
+```bibtex
+@misc{vcoder2026,
+  title={VCoder: Python Code Generation Model},
+  author={Varunesh V, Prawin R K, Sarguru N},
+  year={2026},
+  base_model={Qwen2.5-Coder-3B-Instruct}
+}
+```