license, tags, datasets, language, base_model, pipeline_tag, library_name

license	tags	datasets	language	base_model	pipeline_tag	library_name
apache-2.0	Thinking: Disabled Forge code mot stem coder trl	prithivMLmods/Open-Omega-Forge-1M	en zh	Qwen/Qwen2.5-Coder-3B-Instruct	text-generation	transformers

Omega-Qwen2.5-Coder-3B

Omega-Qwen2.5-Coder-3B is a compact and high-efficiency code-focused model fine-tuned on Qwen2.5-Coder-3B-Instruct, using the symbolic-rich Open-Omega-Forge-1M dataset. Designed specifically for hard-coded tasks and deterministic computation, this model runs in a "thinking-disabled" mode—delivering precise, structured outputs with minimal hallucination, making it ideal for rigorous coding workflows and embedded logic applications.

Thinking: Disabled

[!note] GGUF: https://huggingface.co/prithivMLmods/Omega-Qwen2.5-Coder-3B-GGUF

Key Features

1. Purpose-Built for Hard Coding Specially tuned to perform precise, low-level code generation with minimal reasoning overhead. Ideal for edge-case algorithms, embedded scripting, and deterministic logic patterns.

2. Optimized Qwen2.5 Foundation Built on Qwen2.5-Coder-3B-Instruct, benefiting from its robust token handling, instruction following, and multilingual code representation.

3. Backed by Open-Omega-Forge-1M Dataset Trained on a curated mix of code, math, and logic problems focused on symbolic clarity and STEM coherence, drawn from sources like OpenCodeReasoning, MathX-5M, OpenMathReasoning, and more.

4. Thinking Disabled Mode The model avoids overgeneralizing or injecting speculative reasoning. It executes tasks as-is—perfect for structured prompts, tight constraints, and automation pipelines.

5. Structured Output Control Outputs in JSON, YAML, Python, Markdown, and LaTeX, tailored for script generation, data serialization, and scientific formatting.

6. Efficient 3B Deployment Lightweight and scalable for mid-tier GPUs, offline dev environments, or local inference systems, while maintaining solid performance on symbolic tasks.

---

Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Omega-Qwen2.5-Coder-3B"

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

prompt = "Write a Python function to compute factorial iteratively."

messages = [
    {"role": "system", "content": "You are a deterministic code generator. No assumptions. No extra explanations."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=256
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

---

Intended Use

• Embedded logic and deterministic function generation
• Script automation and toolchain integration
• Codegen under fixed constraints or symbolic inputs
• Lightweight STEM applications on edge devices or offline clusters
• Tools where "no thinking" = better stability

Limitations

• Not suitable for high-level reasoning or open-ended thought processes
• General chat performance is minimal by design
• Lacks emotional intelligence or creative composition capability
• Assumes user provides clear, explicit instructions for best results