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Model: varuneshv/VCoder
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-Coder-3B-Instruct
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- code-generation
+- python
+- qwen
+- unsloth
+- transformers
+- coding-assistant
+language:
+- en
+---
+
+# 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
+
+![Output](https://cdn-uploads.huggingface.co/production/uploads/6a297050d3837ea7b12cc42f/BV8FY6fJN7KQ43jcpC6hr.png)
+
+### 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
+
+---
+
+## 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}
+}
+```
+Github : https://github.com/varunesh-v
+Mail : varunesh.wrk@gmail.com

Run

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+Step1:
+!pip install -U transformers
+
+
+step2:
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("varuneshv/VCoder")
+
+model = AutoModelForCausalLM.from_pretrained(
+    "varuneshv/VCoder"
+)
+
+step3:
+
+model = model.to("cuda")
+
+inputs = tokenizer(
+    "write a python code to merge 3 arrays",
+    return_tensors="pt"
+).to("cuda")
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=100
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))

added_tokens.json

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+  "<|file_sep|>": 151664,
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+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 36,
+  "model_type": "qwen2",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 32768,
+  "tie_word_embeddings": true,
+  "torch_dtype": "float16",
+  "transformers_version": "4.51.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "do_sample": true,
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+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.51.3"
+}

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special_tokens_map.json

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tokenizer.json

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+size 11421896

tokenizer_config.json

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