GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
sargurun16
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
sargurun16
Model Tree
Qwen/Qwen2.5-Coder-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| 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 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.
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% |
text
+7.0% Pass@1 improvementThis demonstrates that the fine-tuned model performs better on Python coding tasks than the original base model.
python
prompt = """### Instruction:Write a Python function to reverse a string. ### Input: ### Response:"""python
def reverse_string(text): return text[::-1]Compatible with:
Dataset used:
Python Code Instructions 15K
The dataset contains instruction-response pairs focused on Python programming tasks including:
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}}Github : https://github.com/sarguru16 Mail : sarguru1609@gmail.com
prompt = """### Instruction:Write a Python function to reverse a string. ### Input: ### Response:"""def reverse_string(text): return text[::-1]@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}}