---
library_name: transformers
base_model: Qwen/Qwen3.5-9B
tags:
  - qwen3.5
  - code
  - agent
  - sft
  - omnicoder
  - tesslate
license: apache-2.0
language:
  - en
pipeline_tag: text-generation
model-index:
  - name: OmniCoder-9B
    results:
      - task:
          type: text-generation
        dataset:
          name: AIME 2025
          type: custom
        metrics:
          - name: pass@5
            type: accuracy
            value: 90.0
      - task:
          type: text-generation
        dataset:
          name: GPQA Diamond
          type: custom
        metrics:
          - name: pass@1
            type: accuracy
            value: 83.8
          - name: pass@3
            type: accuracy
            value: 86.4
      - task:
          type: text-generation
        dataset:
          name: Terminal-Bench 2.0
          type: custom
        metrics:
          - name: Pass Rate
            type: accuracy
            value: 28.1
---

<div align="center">

<img src="omnicoder-banner.png" alt="OmniCoder" width="720">

# OmniCoder-9B

### A 9B coding agent fine-tuned on 425K agentic trajectories.

[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Base Model](https://img.shields.io/badge/Base-Qwen3.5--9B-purple)](https://huggingface.co/Qwen/Qwen3.5-9B)
[![GGUF](https://img.shields.io/badge/GGUF-Available-green)](https://huggingface.co/Tesslate/OmniCoder-9B-GGUF)

!! 3/12/26 Update -> [Install For Your Coding Agents](https://tesslate.com/install#omnicoder)

[Get Started](#quickstart) | [Benchmarks](#benchmarks) | [GGUF Downloads](https://huggingface.co/Tesslate/OmniCoder-9B-GGUF)

---

</div>

## Overview

**OmniCoder-9B** is a 9-billion parameter coding agent model built by [Tesslate](https://tesslate.com), fine-tuned on top of [Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B)'s hybrid architecture (Gated Delta Networks interleaved with standard attention). It was trained on **425,000+ curated agentic coding trajectories** spanning real-world software engineering tasks, tool use, terminal operations, and multi-step reasoning.

The training data was specifically built from **Claude Opus 4.6 agentic and coding reasoning traces**, targeting scaffolding patterns from Claude Code, OpenCode, Codex, and Droid. The dataset includes successful trajectories from models like Claude Opus 4.6, GPT-5.4, GPT-5.3-Codex, and Gemini 3.1 Pro.

The model shows strong agentic behavior: it recovers from errors (read-before-write), responds to LSP diagnostics, and uses proper edit diffs instead of full rewrites. These patterns were learned directly from the real-world agent trajectories it was trained on.

### Key Features

- **Trained on Frontier Agent Traces** : Built from Claude Opus 4.6, GPT-5.3-Codex, GPT-5.4, and Gemini 3.1 Pro agentic coding trajectories across Claude Code, OpenCode, Codex, and Droid scaffolding
- **Hybrid Architecture** : Inherits Qwen3.5's Gated Delta Networks interleaved with standard attention for efficient long-context processing
- **262K Native Context** : Full 262,144 token context window, extensible to 1M+
- **Error Recovery** : Learns read-before-write patterns, responds to LSP diagnostics, and applies minimal edit diffs instead of full rewrites
- **Thinking Mode** : Supports `<think>...</think>` reasoning chains for complex problem decomposition
- **Apache 2.0** : Fully open weights, no restrictions

---

## Benchmarks

<div align="center">

| Benchmark | **OmniCoder-9B** | Qwen3.5-9B | Qwen3-Next-80B | GPT-OSS-120B | GPT-OSS-20B | GLM-4.7-Flash | GLM 4.7 | Claude Haiku 4.5 |
|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| **AIME 2025** (pass@5) | 90 | | | | 91.7 | 91.6 | | |
| **GPQA Diamond** (pass@1) | **83.8** | 81.7 | 77.2 | 80.1 | 71.5 | | | 73 |
| **GPQA Diamond** (pass@3) | **86.4** | | | | | | | |
| **Terminal-Bench 2.0** | **23.6** | 14.6 | | | | | 33.4 | 27 |

</div>

- **GPQA Diamond pass@1: 83.8%** (166/198). +2.1 points over the Qwen3.5-9B base model (81.7). At pass@3: **86.4** (171/198).
- **AIME 2025 pass@5: 90%** (27/30).
- **Terminal-Bench 2.0: 23.6%** (21/89). +8.99 points (+61% improvement) over the Qwen3.5-9B base model (14.6%, 13/89).

---

## Quickstart

### Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Tesslate/OmniCoder-9B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Write a Python function to find the longest common subsequence of two strings."},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=2048, temperature=0.6, top_p=0.95, top_k=20)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[-1]:], skip_special_tokens=True))
```

### vLLM

```bash
vllm serve Tesslate/OmniCoder-9B --tensor-parallel-size 1 --max-model-len 65536
```

```python
from openai import OpenAI

client = OpenAI(base_url="http://localhost:8000/v1", api_key="token")
response = client.chat.completions.create(
    model="Tesslate/OmniCoder-9B",
    messages=[{"role": "user", "content": "Explain the difference between a mutex and a semaphore."}],
    temperature=0.6,
)
print(response.choices[0].message.content)
```

### llama.cpp (GGUF)

```bash
llama-cli --hf-repo Tesslate/OmniCoder-9B-GGUF --hf-file omnicoder-9b-q4_k_m.gguf -p "Your prompt" -c 8192
```

All quantizations: [Tesslate/OmniCoder-9B-GGUF](https://huggingface.co/Tesslate/OmniCoder-9B-GGUF)

---

## Training Details

| | |
|:---|:---|
| **Base Model** | [Qwen3.5-9B](https://huggingface.co/Qwen/Qwen3.5-9B) |
| **Method** | LoRA SFT (r=64, alpha=32) |
| **Dataset** | 425K agentic trajectories from 5 sources |
| **Packing** | Sample packing with 99.35% efficiency |
| **Hardware** | 4x NVIDIA H200 (DDP) |
| **Framework** | Axolotl |
| **Precision** | bf16 |
| **Optimizer** | AdamW (lr=2e-4, cosine schedule) |

---

## Architecture

OmniCoder inherits Qwen3.5-9B's hybrid architecture:

- **Gated Delta Networks** : Linear attention layers interleaved with standard attention for efficient long-range dependencies
- **VLM Backbone** : Built on `Qwen3_5ForConditionalGeneration`

---

## Recommended Sampling Parameters

| Parameter | Value |
|:---|:---|
| Temperature | 0.6 |
| Top-P | 0.95 |
| Top-K | 20 |
| Presence Penalty | 0.0 |

For agentic / tool-calling tasks, consider lower temperature (0.2-0.4) for more deterministic behavior.

---

## Limitations

- Performance on non-English tasks has not been extensively evaluated
- Tool-calling format is flexible but works best with the scaffolding patterns seen in training

---

## Acknowledgments

Special thanks to the [Axolotl](https://github.com/axolotl-ai-cloud/axolotl) team and the discussion in [axolotl#3453](https://github.com/axolotl-ai-cloud/axolotl/issues/3453) for helping get Qwen3.5 packing support working.

---

## Citation

```bibtex
@misc{omnicoder2025,
  title={OmniCoder-9B: A Frontier Open Coding Agent},
  author={Tesslate},
  year={2025},
  url={https://huggingface.co/Tesslate/OmniCoder-9B}
}
```

---

<div align="center">

**Built by [Tesslate](https://tesslate.com)**

</div>