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.

GGUF: https://huggingface.co/prithivMLmods/Theta-Crucis-0.6B-Turbo1-GGUF

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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.

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Quickstart with Transformers

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)

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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

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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

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References

1. Qwen2.5 Technical Report (2024)
2. YaRN: Efficient Context Window Extension of Large Language Models
3. open-r1/Mixture-of-Thoughts