README.md

metadata

license: other
license_name: ztech-license
license_link: LICENSE
language:
  - en
pipeline_tag: text-generation

---

🧮 OmniMath-2B

OmniMath-2B-Pro is an improved and compact model capable of strong mathematical analysis, debugged based on the Qwen3.5‑2B hybrid architecture (Gated Delta Networks interleaved with standard attention). Trained on the basis of 100,000+ Carefully selected mathematical problems from various datasets, it does an excellent job with step-by-step solutions, problems with arithmetic words, geometric reasoning and error recovery.

The main feature of OmniMath-2B-Pro is its ability to solve Olympiad tasks.

Unlike the previous version, the new OmniMath Pro shows the best results on mathematical benchmarks.

Despite its small size, OmniMath-2B-Pro demonstrates high performance and is ideal for resource-constrained and advanced deployment environments.

---

✨ Key Features

• Efficient 2B Scale : Only 2 billion parameters – runs smoothly on a single T4 GPU or even CPU with quantization.
• Step‑by‑Step Reasoning : Trained with explicit <think>...</think>‑style chain‑of‑thought prompts.
• Hybrid Architecture : Inherits Qwen3.5's Gated Delta Networks for efficient long‑context processing.

---

📊 Benchmarks

Preliminary results (evaluation ongoing).

Model	Size (params)	GSM8K Accuracy
Qwen2.5-Math-1.5B	1.5B	54%
Phi-2 (0-shot CoT)	2.7B	50.0%
OmniMath-2B (0-shot CoT)	2B	63.76%
OmniMath-2B-Pro (0-shot CoT)	2B	???%
dolphin-2_6-phi-2	2.7B	58.07%
Qwen2.5-0.5B-Instruct	2.7B	49.6%
gemma-3-1b-it	1.1B	62.8%
MobileLLM-R1.5 950M	1B	52.8%
Gemma 2 2B IT	2B	23.9%

Benchmarks	Qwen2.5 Math 1.5B	Phi 2	OmniMath 2B	OmniMath 2B Pro	dolphin 2_6 phi-2	gemma 3 1b it
GSM8K	54%	50.0%	63.76%	???%	58.07%	62.8%

Updates coming soon.

---

🚀 Quickstart

🤗 Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "ZirTech/OmniMath-2B-Pro"  

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 math assistant. Solve problems step by step."},
    {"role": "user", "content": "A store sells apples for $2 each. If you buy 5 apples, how much do you pay?"}
]

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=256, 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 ZirTech/OmniMath-2B-Pro --tensor-parallel-size 1 --max-model-len 4096

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "ZirTech/OmniMath-2B-Pro"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)
model.eval()

def ask(question):
    prompt = f"<|im_start|>system\nYou are a helpful math assistant.<|im_end|>\n<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant\n"
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.0, do_sample=False)
    response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
    if "user" in response:
        response = response.split("user")[0].strip()
    return response

print(ask("Find the degree for the given field extension Q(sqrt(2), sqrt(3), sqrt(18)) over Q. Give me the answer."))

---

🏗️ Architecture

OmniMath‑2B-Pro fully preserves Qwen3.5‑2B's design:

• Gated Delta Networks : Linear attention layers interleaved with standard attention.

• 262K Native Context : Supports up to 262,144 tokens (extendable with YaRN).

• Built on Qwen3_5ForCausalLM : Seamless integration with Hugging Face ecosystem.

---

⚠️ Limitations

• Numerical accuracy may occasionally falter – always double‑check critical calculations.

• Geometry with visual elements was only trained on textual descriptions; performance on image‑based geometry is limited.

• Non‑English math problems are not thoroughly evaluated.

---

🙏 Acknowledgments

• Qwen Team for the outstanding Qwen3.5 base models.

• Hugging Face for dataset hosting and the Transformers library.

• Thank the community for supporting OmniMath-2B model.

---

📖 Citation

@misc{omnimathpro2b2026,
  title={OmniMath-2B-Pro: A Strong and Lightweight Open-Source Mathematical Model},
  author={Zirt Techniques},
  year={2026},
  url={https://huggingface.co/ZirTech/OmniMath-2B-Pro}
}

---

Built by Zirt Tech ❤️