Update README.md

README.md

@@ -12,4 +12,93 @@ base_model:
 - Qwen/Qwen2.5-Coder-3B-Instruct
 pipeline_tag: text-generation
 library_name: 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](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**
+
+```python
+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