my-ai-stack/Stack-2-9-finetuned

Stack 2.9 - Fine-tuned Code Assistant

A fine-tuned version of Qwen2.5-Coder-1.5B trained on Stack Overflow data

Stack 2.9 is a code generation model fine-tuned from Qwen2.5-Coder-1.5B on Stack Overflow Q&A data for improved programming assistance.

Model Overview

Attribute	Value
Base Model	Qwen/Qwen2.5-Coder-1.5B
Parameters	1.5B
Fine-tuning	LoRA (Rank 8)
Context Length	32,768 tokens
License	Apache 2.0
Release Date	April 2026

Key Capabilities

• Code Generation: Write Python, SQL, JavaScript, TypeScript, and more
• Code Completion: Complete functions, classes, and snippets
• Debugging: Help identify and fix bugs in code
• Code Explanation: Explain and document code
• Programming Q&A: Answer programming questions

Quick Links

• GitHub Repository
• HuggingFace Space (Demo)
• Base Model

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Quickstart

Requirements

pip install transformers>=4.40.0 torch>=2.0.0 accelerate

Python Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "my-ai-stack/Stack-2-9-finetuned"

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Create chat messages
messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Write a Python function to calculate fibonacci numbers"}
]

# Apply chat template
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

# Generate
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512,
    temperature=0.7,
    do_sample=True
)

# Decode response
response = tokenizer.decode(
    generated_ids[0][len(model_inputs.input_ids[0]):],
    skip_special_tokens=True
)
print(response)

Interactive Chat

# See chat.py in repository
python chat.py

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Hardware Requirements

Configuration	GPU	VRAM
1.5B (FP16)	RTX 3060+	~4GB
1.5B (8-bit)	RTX 3060+	~2GB
1.5B (4-bit)	Any modern GPU	~1GB
1.5B (CPU)	None	~8GB RAM

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Training Details

• Method: LoRA (Low-Rank Adaptation)
• LoRA Rank: 8
• LoRA Alpha: 16
• Target Modules: All linear layers (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj)
• Epochs: ~0.8
• Final Loss: 0.0205
• Data Source: Stack Overflow Q&A (Python-heavy)

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Limitations

• Model Size: At 1.5B parameters, smaller than state-of-the-art models (7B, 32B)
• Training Data: Primarily Python-focused; other languages may have lower quality
• Hallucinations: May occasionally generate incorrect code; verification recommended
• No Tool Use: This is a base model without tool-calling capabilities

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Citation

@misc{my-ai-stack/stack-2-9-finetuned,
  author = {Walid Sobhi},
  title = {Stack 2.9: Fine-tuned Qwen2.5-Coder-1.5B on Stack Overflow Data},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/my-ai-stack/Stack-2-9-finetuned}
}

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License

Licensed under the Apache 2.0 license. See LICENSE for details.

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