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---
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
---
# **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) |