Tiny-OR1-Rust-bnb-4bit

Daemontatox/Tiny-OR1-Rust (Quantized)

Description

This model is a quantized version of the original model Daemontatox/Tiny-OR1-Rust.

It's quantized using the BitsAndBytes library to 4-bit using the bnb-my-repo space.

Quantization Details

• Quantization Type: int4
• bnb_4bit_quant_type: nf4
• bnb_4bit_use_double_quant: True
• bnb_4bit_compute_dtype: bfloat16
• bnb_4bit_quant_storage: uint8

📄 Original Model Information

Tiny-OR1-Rust

A lightweight Rust code assistant model for code generation, completion, and explanation.

Model Description

Tiny-OR1-Rust is a specialized language model fine-tuned from Qwen3-1.7B for Rust programming tasks. Built on the efficient Qwen3 architecture, this 1.7B parameter model provides effective code generation, completion, and explanation capabilities specifically tailored for the Rust programming language while maintaining a compact footprint.

Model Details

• Model Name: Tiny-OR1-Rust
• Developer: Daemontatox
• Model Type: Code Generation / Text-to-Code
• Language: Rust
• Architecture: Qwen3-based Transformer
• Parameters: 1.7B
• Base Model: Qwen3-1.7B
• Training Dataset: Tesslate/Rust_Dataset

Intended Use

Primary Use Cases

• Code Generation: Generate Rust code from natural language descriptions
• Code Completion: Complete partial Rust code snippets
• Code Explanation: Explain Rust code functionality and concepts
• Learning Assistant: Help developers learn Rust programming patterns and best practices

Intended Users

• Rust developers and learners
• Students studying systems programming
• Developers transitioning to Rust from other languages
• Code editors and IDEs integrating Rust assistance

How to Use

Basic Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Daemontatox/Tiny-OR1-Rust")
model = AutoModelForCausalLM.from_pretrained("Daemontatox/Tiny-OR1-Rust")

# Example prompt
prompt = "Write a Rust function to calculate factorial:"

# Generate code
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(inputs, max_length=150, temperature=0.7, pad_token_id=tokenizer.eos_token_id)
generated_code = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_code)

Prompt Examples

Code Generation:

"Write a Rust function that reads a file and counts the number of lines:"
"Create a Rust struct for a binary tree with insert and search methods:"
"Implement a thread-safe counter using Arc and Mutex in Rust:"

Code Explanation:

"Explain this Rust code: fn main() { let x = vec![1, 2, 3]; }"
"What does the ? operator do in Rust error handling?"

Training Data

The model was trained on the Tesslate/Rust_Dataset, which contains:

• Rust source code from various projects
• Code documentation and comments
• Rust programming examples and tutorials
• Community-contributed Rust code snippets

Performance

The model demonstrates strong performance in:

• Generating syntactically correct Rust code
• Understanding Rust-specific concepts (ownership, borrowing, lifetimes)
• Providing contextually appropriate code completions
• Explaining Rust programming patterns

Limitations

• Domain Specificity: Optimized for Rust code; may not perform well on other programming languages
• Model Size: Being a "tiny" model, it may have limitations with very complex code generation tasks
• Context Length: Limited context window may affect performance on very long code sequences
• Specialized Knowledge: May not have extensive knowledge of very recent Rust features or niche crates

Ethical Considerations

• The model generates code based on training data patterns and may reproduce coding practices from the dataset
• Users should review and test generated code before using in production environments
• The model should not be used as a substitute for understanding fundamental programming concepts

License

[Specify license - e.g., MIT, Apache 2.0, etc.]

Citation

@misc{tiny-or1-rust,
  title={Tiny-OR1-Rust: A Lightweight Rust Code Assistant Based on Qwen3},
  author={Daemontatox},
  year={2024},
  howpublished={\url{https://huggingface.co/Daemontatox/Tiny-OR1-Rust}},
  note={Fine-tuned from Qwen3-1.7B on Tesslate/Rust_Dataset}
}

Contact

For questions, issues, or contributions, please contact [your contact information or GitHub profile].

Acknowledgments

• Thanks to the Tesslate team for providing the Rust dataset
• Built upon the excellent Qwen3-1.7B foundation model by Alibaba Cloud
• Special recognition to the Rust community for their contributions to open-source Rust code

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This model is part of ongoing efforts to make Rust programming more accessible through AI assistance.