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README.md
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license: apache-2.0
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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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---
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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)
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