OmniCoder-9B

Image: OmniCoder

OmniCoder-9B#

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

License Base Model GGUF

!! 3/12/26 Update -> Install For Your Coding Agents

Get Started | Benchmarks | GGUF Downloads

Overview#

OmniCoder-9B is a 9-billion parameter coding agent model built by Tesslate, fine-tuned on top of 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#

• 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#

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#

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

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)#

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

Training Details#

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#

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 team and the discussion in axolotl#3453 for helping get Qwen3.5 packing support working.

Citation#

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

Built by Tesslate