---
base_model: Qwen/Qwen3.5-9B
tags:
  - text-generation-inference
  - transformers
  - unsloth
  - qwen3_5
  - code
  - fine-tune
  - gguf
  - conversational
  - autotrain_compatible
license: apache-2.0
language:
- en
datasets:
- FlameF0X/agentic-code
library_name: transformers
---

# Koa AI v2 (`vamazing/Koa-AI-v2-code-9B`)

**Koa AI v2** is an advanced, instruction-tuned language model engineered for agentic workflows, complex code synthesis, multi-turn tool interaction, and step-by-step technical reasoning.
It is a fine-tuned 9B parameter language model built on the Qwen 3.5 9B architecture. It is optimized for lightweight text generation and coding tasks.

This repository provides both **16-bit merged weights (`.safetensors`)** exported directly from **`checkpoint-270`** (optimal loss: `0.5614`).

---

## 🛠️ Model Overview & Specifications

| Feature | Specification |
| :--- | :--- |
| **Model Name** | Koa AI v2 (Code) |
| **Base Architecture** | Qwen 3.5 9B |
| **Parameters** | 9 Billion |
| **Precision Formats** | 16-bit Merged (`bf16`) |
| **Context Length** | 32,768 tokens native (Fine-tuned at 2,048 sequence cap) |
| **Fine-Tuning Method** | QLoRA (`r = 16`, `alpha = 32`, Dropout = `0.0`) |
| **Target Modules** | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |
| **Primary Frameworks** | Unsloth, PyTorch, Hugging Face Transformers, `llama.cpp` |


---

## 📡 Modalities & Capabilities

### Supported Modalities
* **Text Input → Text/Code Output**: Structured reasoning, code synthesis, documentation, and agentic trajectory logging.
* **Tool & Function Calling**: Formatted structured output for executing terminal/bash commands, tool calls, and API integrations.

### Core Capabilities
* **Agentic Coding & Execution**: Fine-tuned on agentic interaction traces to analyze system states, execute terminal commands, write code, and autonomously debug execution errors.
* **Qwen 3.5 9B Foundation**: Leverages deep multi-step problem solving across complex multi-file codebases and algorithm challenges.
* **Structured Reasoning**: Native support for deep logic, architectural planning, and structured chain-of-thought processing.


---

##  Quickstart

### Option 1: Python / Transformers (16-bit Safetensors)

```bash
pip install transformers torch accelerate unsloth
```

```python
from unsloth import FastLanguageModel

# 1. Load the model and tokenizer
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = "your-username/Koa-AI-v1",
    max_seq_length = 2048,
    load_in_4bit = True,
)
FastLanguageModel.for_inference(model)

# 2. Define prompt using ChatML template
messages = [
    {"role": "system", "content": "You are Koa AI v1, an expert coding agent."},
    {"role": "user", "content": "Write a Python script to monitor GPU VRAM usage."},
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize = True,
    add_generation_prompt = True,
    return_tensors = "pt"
).to("cuda")

# 3. Generate response
outputs = model.generate(input_ids = inputs, max_new_tokens = 512, use_cache = True)
print(tokenizer.decode(outputs[0]))
```

This qwen3_5 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)