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Model: amkyawdev/amk-coder-v2 Source: Original Platform
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README.md
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README.md
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---
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pipeline_tag: text-generation
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license: apache-2.0
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tags:
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- code-generation
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- myanmar
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- burmese
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- qwen
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- qwen2
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- qwen2.5
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- qwen2.5-coder
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- transformers
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- conversational
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- text-generation
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library_name: transformers
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inference:
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parameters:
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max_new_tokens: 512
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temperature: 0.2
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top_p: 0.95
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repetition_penalty: 1.1
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model-index:
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- name: amk-coder-v2
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results:
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- task:
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type: text-generation
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name: CodeGeneration
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dataset:
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name: HumanEval
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type: openai/openai_humaneval
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metrics:
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- type: pass_at_1
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value: 50
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verified: false
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- type: pass_at_10
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value: 75
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verified: false
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- task:
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type: text-generation
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name: PythonCodeGeneration
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dataset:
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name: MBPP
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type: abdshhayan/MBPP
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metrics:
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- type: pass_at_1
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value: 55
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verified: false
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---
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# 🤖 amk-coder-v2 — Myanmar Coding Agent
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Myanmar Coding Assistant — Fine-tuned from **Qwen2.5-Coder-1.5B** using **LoRA (PEFT)**
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---
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## 📋 Table of Contents
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- [Model Overview](#model-overview)
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- [Training Details](#training-details)
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- [Quick Start](#quick-start)
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- [Usage Examples](#usage-examples)
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- [API Deployment](#api-deployment)
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- [Limitations](#limitations)
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- [License](#license)
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---
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## Model Overview
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**amk-coder-v2** is a Myanmar-localized coding assistant fine-tuned from **Qwen2.5-Coder-1.5B** using LoRA (PEFT) technique.
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| Attribute | Value |
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|---|---|
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| **Base Model** | Qwen2.5-Coder-1.5B |
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| **Parameters** | 2B (2,000M) |
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| **Architecture** | Qwen2ForCausalLM |
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| **Training Method** | LoRA (PEFT) fine-tuning |
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| **Dataset** | [amkyawdev/mm-llm-coder-agent-dataset](https://huggingface.co/datasets/amkyawdev/mm-llm-coder-agent-dataset) (4M rows) |
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| **Context Length** | 32,768 tokens |
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| **Format** | Safetensors (BF16) |
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| **License** | Apache-2.0 |
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| **Languages** | Burmese + English |
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### Features
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| Feature | Description |
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|---|---|
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| 🇲🇲 **Myanmar Support** | Full support for Myanmar Unicode text |
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| 💻 **Code Generation** | Python, JavaScript, C++, Java, and more |
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| 🐛 **Debugging** | Bug detection and fixes |
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| 📖 **Code Explanation** | Line-by-line explanations |
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---
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## Training Details
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| Parameter | Value |
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|---|---|
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| **Framework** | Transformers + PEFT |
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| **Training Method** | LoRA fine-tuning |
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| **Target Modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
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| **Optimizer** | paged_adamw_8bit |
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| **Learning Rate** | 3e-5 |
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| **Epochs** | 3 |
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| **Batch Size** | 8 |
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| **Max Length** | 2048 |
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| **Precision** | FP16 mixed |
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| **Hardware** | Kaggle Dual T4 GPU |
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| **Training Time** | ~3-5 hrs |
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### Chat Template (ChatML)
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```
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<|im_start|>system
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You are an expert Myanmar AI coding agent with tool access.<|im_end|>
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<|im_start|>user
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{Instruction}
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Tools available: {Tools}<|im_end|>
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<|im_start|>assistant
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Thought & Code:
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```
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---
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## Quick Start
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### Using Transformers (Python)
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```python
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# Method 1: Pipeline (Recommended for beginners)
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from transformers import pipeline
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pipe = pipeline("text-generation", model="amkyawdev/amk-coder-v2")
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messages = [
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{"role": "user", "content": "Python function တစ်ခုရေးပါ။ list comprehension နဲ့ sorting လုပ်ပေးပါ။"}
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]
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result = pipe(messages, max_new_tokens=512, temperature=0.2)
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print(result[0]['generated_text'])
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# Method 2: Direct Model Loading
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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tokenizer = AutoTokenizer.from_pretrained("amkyawdev/amk-coder-v2")
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model = AutoModelForCausalLM.from_pretrained(
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"amkyawdev/amk-coder-v2",
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torch_dtype=torch.bfloat16,
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device_map="auto"
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)
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messages = [
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{"role": "system", "content": "You are a helpful coding assistant."},
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{"role": "user", "content": "Write a Python function to reverse a string"}
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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outputs = model.generate(inputs, max_new_tokens=512, temperature=0.2)
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response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
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print(response)
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```
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### Using vLLM (Production)
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```bash
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# Install vLLM
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pip install vllm
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# Start server
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vllm serve "amkyawdev/amk-coder-v2" --tensor-parallel-size 1
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# API call
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curl -X POST "http://localhost:8000/v1/chat/completions" \
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-H "Content-Type: application/json" \
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-d '{
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"model": "amkyawdev/amk-coder-v2",
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"messages": [
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{"role": "user", "content": "Hello, write Python code"}
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],
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"max_tokens": 512,
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"temperature": 0.2
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}'
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```
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### Using SGLang
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```bash
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# Install SGLang
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pip install sglang
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# Start server
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python -m sglang.launch_server --model-path "amkyawdev/amk-coder-v2" --port 30000
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# API call
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curl -X POST "http://localhost:30000/v1/chat/completions" \
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-H "Content-Type: application/json" \
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-d '{
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"model": "amkyawdev/amk-coder-v2",
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"messages": [{"role": "user", "content": "Write a hello world in Python"}]
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}'
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```
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---
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## Usage Examples
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### 🇲🇲 Myanmar Prompts
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```python
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messages = [
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{"role": "user", "content": "Python function တစ်ခုရေးပါ။ ဂဏန်းတွေကို sorting လုပ်ပေးပါ။"}
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]
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# Output: def sort_numbers(numbers): return sorted(numbers)
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```
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### 🇬🇧 English Prompts
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```python
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messages = [
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{"role": "user", "content": "Explain this code:\nfor i in range(10):\n print(i)"}
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]
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# Output: This is a for loop that prints numbers 0 to 9
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```
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### 🐛 Debugging
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```python
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messages = [
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{"role": "user", "content": "Fix this Python code:\nprint('Hello' + 5)"}
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]
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# Output: TypeError fix suggestion with corrected code
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```
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---
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## API Deployment
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### Backend Server
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```bash
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cd backend
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pip install -r requirements.txt
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export HF_TOKEN=hf_your_token
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uvicorn app.main:app --host 0.0.0.0 --port 8000
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```
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### Endpoints
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| Method | Endpoint | Description |
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|---|---|---|
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| GET | `/` | Health check |
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| GET | `/health` | Service health status |
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| POST | `/chat` | Streaming chat (SSE) |
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| GET | `/demo` | Demo HTML interface |
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| GET | `/models` | Model information |
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### Request Format
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```bash
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# Streaming chat
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curl -X POST "http://localhost:8000/chat" \
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-H "Content-Type: application/json" \
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-d '{
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"messages": [
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{"role": "user", "content": "Write a Fibonacci function in Python"}
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],
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"stream": true
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}'
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```
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### Docker Deployment
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```bash
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# Using Docker Model Runner
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docker model run hf.co/amkyawdev/amk-coder-v2
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# Using vLLM Docker
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docker run --gpus all \
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-v ~/.cache/huggingface:/root/.cache/huggingface \
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-p 8000:8000 \
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--rm \
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vllm/vllm-openai:latest \
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--model amkyawdev/amk-coder-v2
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```
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---
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## ⚠️ Limitations
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1. **Context Length** - Maximum 32,768 tokens
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2. **Code Quality** - May generate incorrect code; verify outputs
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3. **Myanmar Unicode** - Best results with proper Zawgyi-to-Unicode conversion
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4. **Domain Knowledge** - Limited to common programming languages
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5. **Safety** - May produce harmful content; use responsible AI practices
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---
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## 📖 Resources
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- [Qwen2.5-Coder Documentation](https://qwenlm.github.io/blog/Qwen2.5-Coder/)
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- [Transformers Library](https://huggingface.co/docs/transformers)
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- [HuggingFace Hub](https://huggingface.co/amkyawdev/amk-coder-v2)
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---
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## 🙏 Acknowledgments
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- **Alibaba Cloud Qwen Team** - Base model Qwen2.5-Coder
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- **HuggingFace** - Model hosting and infrastructure
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- **Myanmar Developer Community** - Testing and feedback
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---
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## 📝 License
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Apache License 2.0 - See LICENSE file for details.
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---
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## 📧 Contact
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- **Author**: amkyawdev
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- **HuggingFace**: [amkyawdev/amk-coder-v2](https://huggingface.co/amkyawdev/amk-coder-v2)
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- **GitHub**: [github.com/amkyawdev](https://github.com/amkyawdev)
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---
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*Made with ❤️ for Myanmar Developers*
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