---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3_moe
- rust
- code-generation
- instruction-tuning
- open-source
library_name: transformers
base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
model_name: Daemontatox/HydraCoder
trained_with: 
- Unsloth
- Hugging Face TRL
---

Daemontatox/HydraCoder

HydraCoder is a state-of-the-art Rust-specialized coding model built on Qwen/Qwen3-Coder-30B-A3B-Instruct, designed for high-fidelity, idiomatic Rust code generation, completion, and repair.

This is the strongest pure Rust model to date, specifically fine-tuned on real-world projects, crates, compiler patterns, and Rust best practices.

🦀 Key Features

Focused on Rust: Trained on diverse idiomatic Rust repositories, including tokio, serde, actix, clap, and async ecosystems.

Instruction-tuned: Accepts natural instructions like "write a TCP server" or "convert this struct to JSON".

Zero-shot Capable: Performs well without examples, and adapts to many Rust-specific patterns like lifetimes, Result<T, E>, traits, ownership, and borrow checking.



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🧠 Intended Use

HydraCoder is ideal for:

Rust code generation from natural instructions

Auto-completion and snippet insertion in editors

Static analysis assistant tools

Compiler plugin or LSP augmentation

Refactoring or code review suggestions

Code repair or bugfix generation



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🔬 Model Details

Property	Value

Base Model	Qwen/Qwen3-Coder-30B-A3B-Instruct
Fine-tuned Model	Daemontatox/HydraCoder
Model Type	Mixture-of-Experts (2/8 active experts)
Parameters	~30B (with 2 active experts, ~7.5B per step)
Domain Specialization	Idiomatic Rust Code
Training Tooling	Unsloth + Hugging Face TRL
License	Apache 2.0



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✍️ Example Prompt

Prompt:

Write a simple multithreaded web server in Rust that serves "Hello, world!" to any GET request.

HydraCoder Output:

use std::net::{TcpListener, TcpStream};
use std::io::{Read, Write};
use std::thread;

fn handle_client(mut stream: TcpStream) {
    let mut buffer = [0; 1024];
    if let Ok(_) = stream.read(&mut buffer) {
        let response = "HTTP/1.1 200 OK\r\nContent-Type: text/plain\r\n\r\nHello, world!";
        let _ = stream.write(response.as_bytes());
    }
}

fn main() -> std::io::Result<()> {
    let listener = TcpListener::bind("127.0.0.1:7878")?;
    println!("Server listening on port 7878...");

    for stream in listener.incoming() {
        match stream {
            Ok(stream) => {
                thread::spawn(|| {
                    handle_client(stream);
                });
            }
            Err(e) => eprintln!("Connection failed: {}", e),
        }
    }

    Ok(())
}


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⚡ Inference Code

You can run inference using transformers and text-generation pipeline:

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_id = "Daemontatox/HydraCoder"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)

prompt = "Write a function in Rust that takes a list of integers and returns the sum of all even numbers."

output = pipe(prompt, max_new_tokens=200, do_sample=True, temperature=0.2)[0]["generated_text"]
print(output)


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🧪 Benchmarks (Qualitative)

HydraCoder performs especially well on:

Code Rust (HumanEval / MBPP in Rust) – correctly compiling and idiomatic

Leetcode-style Rust tasks

Crate-specific patterns – understands macros, derive attributes, and lifetimes

Ownership-safe solutions



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🔍 Limitations

Trained for Rust only – not suited for general-purpose multi-language tasks.

May hallucinate external crate names or imports if not in prompt.

Not guaranteed to pass Rust compiler unless prompt includes full context.



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✅ License

Released under the Apache 2.0 License. Free for research and commercial use with attribution.


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👨‍💻 Author

Model Developer: Daemontatox

Base Model Author: Qwen Team

Fine-tuned with: Unsloth + TRL



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