Update README.md

README.md

@@ -17,4 +17,95 @@ tags:
 - code
 - math
 - trl
----
+---
+
+# Bootes-Qwen3\_Coder-Reasoning
+
+> Bootes-Qwen3\_Coder-Reasoning is a fine-tuned variant of the Qwen3-4B architecture, optimized for high-accuracy code reasoning and structured logical task completion. Trained on the CodeAlpaca\_20K dataset and additional curated programming corpora, this model is designed to perform technical coding, reasoning, and instruction-following tasks with lightweight computational requirements.
+
+## Key Features
+
+1. Code Reasoning with CodeAlpaca\_20K and More
+   Fine-tuned on CodeAlpaca\_20K and supplementary high-quality datasets focused on:
+
+   * Multi-language programming tasks
+   * Code explanation, completion, and debugging
+   * Instruction-following with step-wise execution logic
+
+2. Cross-Language Code Understanding
+   Handles Python, JavaScript, C++, and more. Ideal for code generation, transformation, bug-fixing, and logic validation.
+
+3. Structured Output Generation
+   Delivers responses in Markdown, JSON, YAML, and structured code blocks. Optimized for IDE workflows, documentation tools, and reproducible computation notebooks.
+
+4. Instruction-Tuned for Developer Use Cases
+   Maintains strong fidelity to user prompts, especially multi-turn or step-by-step technical instructions across engineering and data workflows.
+
+5. Multilingual Reasoning in Technical Domains
+   Capable of technical comprehension and explanation in over 20 human languages, supporting global developer audiences.
+
+6. Efficient 4B Architecture
+   Based on Qwen3-4B for a performance-efficient inference model that scales well on mid-range GPUs and cloud deployment setups.
+
+## Quickstart with Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Bootes-Qwen3_Coder-Reasoning"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Write a Python function to check whether a number is a palindrome. Explain each step."
+
+messages = [
+    {"role": "system", "content": "You are a precise coding and reasoning assistant trained on CodeAlpaca and developer datasets."},
+    {"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=512
+)
+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
+
+* Code generation, completion, and explanation
+* Multi-step algorithmic reasoning
+* Structured technical document generation (Markdown, JSON, YAML)
+* Debugging assistance and refactoring suggestions
+* Technical tutoring and developer assistant workflows
+* Cross-lingual programming education and translation
+
+## Limitations
+
+* May underperform on non-code-related creative writing
+* Limited context window versus larger models
+* Sensitive to prompt phrasing for ambiguous instructions
+* Occasionally over-justifies code when brevity is desired
+
+## References
+
+1. Qwen2.5 Technical Report – [https://arxiv.org/pdf/2412.15115](https://arxiv.org/pdf/2412.15115)
+2. CodeAlpaca Dataset – [https://github.com/sahil280114/codealpaca](https://github.com/sahil280114/codealpaca)
+3. YaRN: Context Window Extension for LLMs – [https://arxiv.org/pdf/2309.00071](https://arxiv.org/pdf/2309.00071)