--- license: apache-2.0 language: - en base_model: - prithivMLmods/Qwen3-0.6B-ft-bf16 pipeline_tag: text-generation library_name: transformers tags: - text-generation-inference - code - moe datasets: - open-r1/Mixture-of-Thoughts --- ![3.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/tCzY2m08LhLrUmcCyLkQu.png) # **Theta-Crucis-0.6B-Turbo1** > **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. > \[!note] > GGUF: [https://huggingface.co/prithivMLmods/Theta-Crucis-0.6B-Turbo1-GGUF](https://huggingface.co/prithivMLmods/Theta-Crucis-0.6B-Turbo1-GGUF) --- ## **Key Features** 1. **MoT Fine-Tuning on Code Expert Clusters** 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. 2. **Turbo Code Generation & Debugging** Excels at generating well-structured, clean code in Python, JavaScript, C++, and more. Capable of explaining logic, identifying bugs, and suggesting improvements. 3. **Structured Output Capabilities** Supports outputs in **Markdown**, **JSON**, **YAML**, and **LaTeX**, making it ideal for auto-documentation, API formatting, and configuration file generation. 4. **Technical Fluency Across Languages** Handles code queries and explanations in over **20 languages**, enabling global developer support and multilingual documentation. 5. **Lightweight, Inference-Optimized Design** Suitable for deployment on **edge devices**, **laptops**, or **VRAM-limited GPUs**, with fast inference and strong accuracy in technical prompts. --- ## **Quickstart with Transformers** ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "prithivMLmods/Theta-Crucis-0.6B-Turbo1" model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) tokenizer = AutoTokenizer.from_pretrained(model_name) prompt = "Write a Python function that checks if a string is a palindrome. Explain each step." messages = [ {"role": "system", "content": "You are an expert code assistant."}, {"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** * Programming education, code synthesis, and debugging support * Structured data and config file generation (e.g., JSON, YAML) * Developer assistant roles in multilingual and technical environments * Deployment on constrained devices with high code output needs * Fast prototyping and script generation across languages --- ## **Limitations** * May underperform in long conversational or abstract language tasks * Context length limitations can restrict multi-file or large project reasoning * Not designed for creative writing or open-ended dialogue * Focuses on technical and structured domains—general fluency is limited --- ## **References** 1. [Qwen2.5 Technical Report (2024)](https://arxiv.org/pdf/2412.15115) 2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071) 3. [open-r1/Mixture-of-Thoughts](https://huggingface.co/datasets/open-r1/Mixture-of-Thoughts)