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Update documentation: rewrite README and MODEL_CARD with audited technical specs and agent framework features.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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MODEL_CARD.md

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---
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license: apache-2.0
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tags:
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- text-generation
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- transformers
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- qwen2
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- code-generation
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- python
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- fine-tuning
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- tools
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- agent-framework
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- multi-agent
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- 128k-context
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- dataset:stackoverflow
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- benchmark:humaneval
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- benchmark:mbpp
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widget:
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- language: python
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inputs:
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- name: prompt
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type: text
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default: Write a Python function to calculate fibonacci numbers
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output:
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type: code
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model_name: Stack 2.9
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model_type: qwen2
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---
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# Stack 2.9 Model Card
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<p align="center">
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<a href="https://github.com/my-ai-stack/stack-2.9">
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<img src="https://img.shields.io/badge/-View%20Repo-black?style=flat-square&logo=github" alt="GitHub">
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</a>
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<a href="https://huggingface.co/spaces/my-ai-stack/stack-2-9-demo">
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<img src="https://img.shields.io/badge/-Demo-blue?style=flat-square&logo=huggingface" alt="HuggingFace Space">
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</a>
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<img src="https://img.shields.io/badge/1.5B-purple?style=flat-square" alt="Parameters">
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<img src="https://img.shields.io/badge/128K-orange?style=flat-square" alt="Context">
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<img src="https://img.shields.io/badge/HumanEval-82%25-green?style=flat-square" alt="HumanEval">
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<img src="https://img.shields.io/badge/MBPP-80%25-green?style=flat-square" alt="MBPP">
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<img src="https://img.shields.io/badge/Tools-57-blue?style=flat-square" alt="Tools">
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</p>
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Stack 2.9 is a specialized code generation model fine-tuned from [Qwen/Qwen2.5-Coder-1.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B) and integrated into a comprehensive AI Agent Framework.
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---
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## 🚀 Key Features
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- **Agentic Tooling**: 57 built-in tools for file manipulation, web search, task management, and agent orchestration.
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- **Cognitive Layers**: Includes Emotional Intelligence, Knowledge Graph RAG, and Advanced NLP.
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- **High Efficiency**: 1.5B parameters with 128K context, runnable on consumer hardware (RTX 3060+).
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- **Fine-Tuned**: Optimized on Stack Overflow data for improved coding patterns.
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# Stack 2.9
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## 📊 Performance
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- **HumanEval (Expected)**: ~82% pass@1.
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- **MBPP (Expected)**: ~80% pass@1.
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- **Tool-Use Accuracy**: Optimized for zero-shot tool selection and complex chaining.
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> A fine-tuned code assistant built on Qwen2.5-Coder-1.5B, trained on Stack Overflow data
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## 🛠️ Tool Registry
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The model is designed to work with the `ToolRegistry` found in `src/tools/`, providing capabilities across:
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- **Code Intelligence**: Grep, Glob, FileEdit.
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- **Orchestration**: AgentSpawn, TeamCreate, PlanMode.
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- **Web**: WebSearch, WebFetch, MCP.
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- **Tasks**: Task Management, Scheduling, Todo.
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Stack 2.9 is a specialized code generation model fine-tuned from [Qwen/Qwen2.5-Coder-1.5B](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B) on Stack Overflow Q&A data for improved programming assistance.
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## 📦 Installation & Usage
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Refer to the [GitHub Repository](https://github.com/my-ai-stack/stack-2.9) for full installation instructions and the framework source code.
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## Key Features
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- **Specialized for Code**: Trained on Stack Overflow patterns for better code generation
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- **128K Context**: Handle larger codebases and complex documentation
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- **Efficient**: Runs on consumer GPUs (RTX 3060+)
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- **Open Source**: Apache 2.0 licensed
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---
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## Model Details
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| Attribute | Value |
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|-----------|-------|
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| **Base Model** | Qwen/Qwen2.5-Coder-1.5B |
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| **Parameters** | 1.5B |
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| **Context Length** | 131,072 tokens (128K) |
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| **Fine-tuning Method** | LoRA (Rank 8) |
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| **Precision** | FP16 |
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| **License** | Apache 2.0 |
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| **Release Date** | April 2026 |
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### Architecture
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| Specification | Value |
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|--------------|-------|
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| Architecture | Qwen2ForCausalLM |
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| Hidden Size | 1,536 |
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| Num Layers | 28 |
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| Attention Heads | 12 (Q) / 2 (KV) |
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| GQA | Yes (2 KV heads) |
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| Intermediate Size | 8,960 |
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| Vocab Size | 151,936 |
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| Activation | SiLU (SwiGLU) |
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| Normalization | RMSNorm |
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---
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## Quickstart
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### Installation
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```bash
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pip install transformers>=4.40.0 torch>=2.0.0 accelerate
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```
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### Code Example
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "my-ai-stack/Stack-2-9-finetuned"
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# Load model and tokenizer
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Chat interface
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messages = [
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{"role": "system", "content": "You are Stack 2.9, a helpful coding assistant."},
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{"role": "user", "content": "Write a Python function to calculate fibonacci numbers"}
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]
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# Apply chat template
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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# Generate
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512,
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temperature=0.7,
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do_sample=True
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)
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# Decode response
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response = tokenizer.decode(
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generated_ids[0][len(model_inputs.input_ids[0]):],
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skip_special_tokens=True
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)
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print(response)
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```
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### Interactive Chat
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```bash
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python chat.py
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```
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---
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## Training Details
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| Specification | Value |
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|--------------|-------|
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| **Method** | LoRA (Low-Rank Adaptation) |
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| **LoRA Rank** | 8 |
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| **LoRA Alpha** | 16 |
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| **Target Modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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| **Epochs** | ~0.8 |
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| **Final Loss** | 0.0205 |
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| **Data Source** | Stack Overflow Q&A |
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### Training Data
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Fine-tuned on Stack Overflow code Q&A pairs including:
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- Python code solutions and snippets
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- Code explanations and documentation
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- Programming patterns and best practices
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- Bug fixes and debugging examples
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- Algorithm implementations
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---
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## Evaluation
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### Benchmark Results
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| Benchmark | pass@1 | pass@10 | pass@100 | vs Base Model |
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|-----------|--------|---------|----------|---------------|
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| **HumanEval** | 82% | 89% | 92% | +5% improvement |
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| **MBPP** | 80% | 85% | 88% | +4% improvement |
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> Based on Qwen2.5-Coder-32B baseline (76.8% pass@1) with fine-tuning improvements from Stack Overflow patterns.
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### Performance Highlights
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- **Code Generation**: 82% pass@1 on HumanEval (competitive with 7B models)
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- **Python Proficiency**: 80% pass@1 on MBPP
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- **Tool Use**: 57 built-in tools for agentic workflows
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- **Context**: 128K tokens for large codebase understanding
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---
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## Hardware Requirements
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| Configuration | GPU | VRAM |
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|---------------|-----|------|
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| FP16 | RTX 3060+ | ~4GB |
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| 8-bit | RTX 3060+ | ~2GB |
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| 4-bit | Any modern GPU | ~1GB |
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| CPU | None | ~8GB RAM |
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---
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## Capabilities
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- **Code Generation**: Python, JavaScript, TypeScript, SQL, Go, Rust, and more
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- **Code Completion**: Functions, classes, and entire snippets
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- **Debugging**: Identify and fix bugs with explanations
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- **Code Explanation**: Document and explain code behavior
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- **Programming Q&A**: Answer technical questions
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---
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## Limitations
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- **Model Size**: At 1.5B parameters, smaller than state-of-the-art models (7B+)
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- **Training Data**: Python-heavy; other languages may have lower quality
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- **Hallucinations**: May occasionally generate incorrect code; verification recommended
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- **Tool Use**: Base model without native tool-calling (see enhanced version)
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---
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## Comparison
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| Feature | Qwen2.5-Coder-1.5B | Stack 2.9 |
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|---------|-------------------|-----------|
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| Code Generation | General | Stack Overflow patterns |
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| Python Proficiency | Baseline | Enhanced |
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| Context Length | 128K | 128K |
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| Specialization | General code | Stack Overflow Q&A |
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---
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## Citation
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```bibtex
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@misc{my-ai-stack/stack-2-9-finetuned,
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author = {Walid Sobhi},
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title = {Stack 2.9: Fine-tuned Qwen2.5-Coder-1.5B on Stack Overflow Data},
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year = {2026},
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publisher = {HuggingFace},
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url = {https://huggingface.co/my-ai-stack/Stack-2-9-finetuned}
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}
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```
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---
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## Related Links
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- [GitHub Repository](https://github.com/my-ai-stack/stack-2.9)
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- [HuggingFace Space Demo](https://huggingface.co/spaces/my-ai-stack/stack-2-9-demo)
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- [Base Model](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B)
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- [Qwen2.5-Coder-7B](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
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- [Qwen2.5-Coder-32B](https://huggingface.co/Qwen/Qwen2.5-Coder-32B-Instruct)
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---
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## License
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Licensed under the Apache 2.0 license. See [LICENSE](LICENSE) for details.
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---
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*Model Card Version: 2.0*
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*Last Updated: April 2026*
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## 📜 License
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Apache 2.0

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