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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- prithivMLmods/Qwen3-0.6B-ft-bf16 |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- code |
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- moe |
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datasets: |
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- open-r1/Mixture-of-Thoughts |
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--- |
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# **Theta-Crucis-0.6B-Turbo1** |
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> **Theta-Crucis-0.6B-Turbo1** is a compact, high-performance model designed for **code generation**, **technical reasoning**, and **structured output tasks**. Fine-tuned from **Qwen3-0.6B** using the **Mixture of Thoughts (MoT)** dataset with an emphasis on **code expert clusters**, this model delivers agile and accurate coding assistance in low-resource environments. At only **0.6B parameters**, it offers strong fluency in programming, structured syntax, and technical language generation. |
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> \[!note] |
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> GGUF: [https://huggingface.co/prithivMLmods/Theta-Crucis-0.6B-Turbo1-GGUF](https://huggingface.co/prithivMLmods/Theta-Crucis-0.6B-Turbo1-GGUF) |
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--- |
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## **Key Features** |
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1. **MoT Fine-Tuning on Code Expert Clusters** |
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Leveraging the **Mixture of Thoughts (MoT)** dataset, this model is fine-tuned on high-quality programming data across languages, debugging patterns, and code reasoning structures. |
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2. **Turbo Code Generation & Debugging** |
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Excels at generating well-structured, clean code in Python, JavaScript, C++, and more. Capable of explaining logic, identifying bugs, and suggesting improvements. |
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3. **Structured Output Capabilities** |
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Supports outputs in **Markdown**, **JSON**, **YAML**, and **LaTeX**, making it ideal for auto-documentation, API formatting, and configuration file generation. |
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4. **Technical Fluency Across Languages** |
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Handles code queries and explanations in over **20 languages**, enabling global developer support and multilingual documentation. |
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5. **Lightweight, Inference-Optimized Design** |
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Suitable for deployment on **edge devices**, **laptops**, or **VRAM-limited GPUs**, with fast inference and strong accuracy in technical prompts. |
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--- |
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## **Quickstart with Transformers** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Theta-Crucis-0.6B-Turbo1" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Write a Python function that checks if a string is a palindrome. Explain each step." |
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messages = [ |
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{"role": "system", "content": "You are an expert code assistant."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |
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--- |
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## **Intended Use** |
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* Programming education, code synthesis, and debugging support |
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* Structured data and config file generation (e.g., JSON, YAML) |
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* Developer assistant roles in multilingual and technical environments |
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* Deployment on constrained devices with high code output needs |
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* Fast prototyping and script generation across languages |
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--- |
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## **Limitations** |
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* May underperform in long conversational or abstract language tasks |
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* Context length limitations can restrict multi-file or large project reasoning |
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* Not designed for creative writing or open-ended dialogue |
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* Focuses on technical and structured domains—general fluency is limited |
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--- |
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## **References** |
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1. [Qwen2.5 Technical Report (2024)](https://arxiv.org/pdf/2412.15115) |
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2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071) |
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3. [open-r1/Mixture-of-Thoughts](https://huggingface.co/datasets/open-r1/Mixture-of-Thoughts) |
