lainlives / OmniCoder-9B-bnb-4bit

Tesslate/OmniCoder-9B (Quantized)

Description

This model is a quantized version of the original model Tesslate/OmniCoder-9B .

Quantization Details

• Quantization Type : int4
• bnb_4bit_quant_type : nf4
• bnb_4bit_use_double_quant : True
• bnb_4bit_compute_dtype : bfloat16
• bnb_4bit_quant_storage : int8

📄 Original Model Information

OmniCoder-9B

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

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

Get Started | Benchmarks | GGUF Downloads

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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}
}