Rhea-4B-Coding-max

Rhea-4B-Coding is an optimized version of Aquiles-ai/Athenea-4B-Coding, specialized in code reasoning, debugging, agentic tools and multi-pass problem solving.

Trained on high-quality programming data with explicit reasoning traces using thinking and 思考结束 tags, the model is designed to perform detailed 3-pass reasoning for software development, algorithm design, and code comprehension tasks:

1. Pass 1: First implementation
2. Pass 2: Self-review for bugs, edge cases, security, performance
3. Pass 3: Final optimized version with identical functionality

⚠️ Important Note: This model uses an uncensored base version, providing full expressive freedom and unrestricted output generation. Users are fully responsible for any use or content produced by the model. It is intended exclusively for research and experimentation purposes.

🎯 Model Description

Rhea-4B-Coding extends Athenea-4B-Coding's structured reasoning capabilities into programming-related domains with multi-pass processing, showing strong performance on logical problem-solving, code completion, debugging scenarios, and iterative code refinement.

Key features:

• Multi-Pass Processing: 3-step reasoning with <think>, <review>, and <final> tags
• Agentic Tools for AI Agents
• Step-by-step code reasoning within thinking blocks
• Self-review capabilities for bug detection and optimization
• Specialization in algorithmic and debugging tasks
• Uncensored output generation for full reasoning visibility
• Improved logical consistency through focused fine-tuning
• Compatible with open inference frameworks (Transformers, vLLM, etc.)

The model was fine-tuned using the dataset Aquiles-ai/Athenea-Coding-100k, which includes diverse programming challenges, structured reasoning chains, and natural language explanations across multiple programming languages.

🔄 Multi-Pass Architecture

The model uses special tokens for structured reasoning:

This structure ensures identical functionality across all passes while improving code structure, comments, and robustness.

💻 Usage

Installation

uv pip install transformers torch accelerate

Basic Inference

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("Roskosmos19/Rhea-4B-Coding",
        dtype=torch.bfloat16,
        trust_remote_code=True,
        device_map="auto",
        attn_implementation="flash_attention_2") # Requires flash-attn

# Without flash-attn:
# model = AutoModelForCausalLM.from_pretrained("Roskosmos19/Rhea-4B-Coding",
#     dtype="auto",
#     device_map="auto"
# )

tokenizer = AutoTokenizer.from_pretrained("Roskosmos19/Rhea-4B-Coding", trust_remote_code=True)

messages = [
    {"role": "user", "content": "Hey, write a Python function that calculates the factorial of a number recursively."}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to('cuda')

with torch.no_grad():
    output = model.generate(
        **inputs,
        max_new_tokens=16384,  # Increased for multi-pass output
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )

# Decode and print the output
print(tokenizer.decode(output[0], skip_special_tokens=False))

Multi-Pass Inference (Recommended)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("Roskosmos19/Rhea-4B-Coding",
        dtype=torch.bfloat16,
        trust_remote_code=True,
        device_map="auto")

tokenizer = AutoTokenizer.from_pretrained("Roskosmos19/Rhea-4B-Coding", trust_remote_code=True)

def generate_multi_pass(prompt, max_tokens_per_pass=4096):
    """
    Generate code with 3-pass reasoning:
    Pass 1: First implementation
    Pass 2: Self-review
    Pass 3: Final optimized version
    """
    
    # Pass 1: First implementation
    messages = [{"role": "user", "content": prompt}]
    inputs = tokenizer.apply_chat_template(
        messages,
        add_generation_prompt=True,
        tokenize=True,
        return_dict=True,
        return_tensors="pt",
    ).to('cuda')
    
    with torch.no_grad():
        output1 = model.generate(
            **inputs,
            max_new_tokens=max_tokens_per_pass,
            temperature=0.4,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id,
        )
    
    pass1 = tokenizer.decode(output1[0], skip_special_tokens=False)
    
    # Pass 2: Self-review
    review_prompt = pass1 + "\n<<think>\n### PASS 2 - Self-Review:\n"
    inputs2 = tokenizer(review_prompt, return_tensors="pt").to('cuda')
    
    with torch.no_grad():
        output2 = model.generate(
            **inputs2,
            max_new_tokens=2048,
            temperature=0.3,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id,
        )
    
    review = tokenizer.decode(output2[0], skip_special_tokens=False)
    
    # Pass 3: Final version
    final_prompt = review + "\n<<final>\n### PASS 3 - Final Version:\n"
    inputs3 = tokenizer(final_prompt, return_tensors="pt").to('cuda')
    
    with torch.no_grad():
        output3 = model.generate(
            **inputs3,
            max_new_tokens=max_tokens_per_pass,
            temperature=0.2,
            pad_token_id=tokenizer.pad_token_id,
            eos_token_id=tokenizer.eos_token_id,
        )
    
    final = tokenizer.decode(output3[0], skip_special_tokens=False)
    
    return {
        "pass1": pass1,
        "review": review,
        "pass3": final
    }

# Example usage
result = generate_multi_pass("Write a Python function for binary search")
print("=== PASS 1 ===")
print(result["pass1"])
print("\n=== REVIEW ===")
print(result["review"])
print("\n=== FINAL ===")
print(result["pass3"])

Streaming Inference

from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import torch
from threading import Thread

model = AutoModelForCausalLM.from_pretrained("Roskosmos19/Rhea-4B-Coding",
        dtype=torch.bfloat16,
        trust_remote_code=True,
        device_map="auto",
        attn_implementation="flash_attention_2")

tokenizer = AutoTokenizer.from_pretrained("Roskosmos19/Rhea-4B-Coding", trust_remote_code=True)

messages = [
    {"role": "user", "content": "Hey, write a Python function that implements the binary search algorithm recursively."}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to('cuda')

# Create the streamer
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=False)

# Build kwargs for generate
generate_kwargs = dict(
    **inputs,
    max_new_tokens=16384,  # Increased for multi-pass output
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
    streamer=streamer,
)

def _generate_thread(model, kwargs):
    with torch.no_grad():
        model.generate(**kwargs)

thread = Thread(target=_generate_thread, args=(model, generate_kwargs))
thread.start()

for chunk in streamer:
    print(chunk, end="", flush=True)

Production Deployment with vLLM

Start server:

vllm serve Roskosmos19/Rhea-4B-Coding \
  --host 0.0.0.0 \
  --port 8000 \
  --api-key dummyapikey \
  --max-model-len=262144 \
  --async-scheduling \
  --gpu-memory-utilization=0.90

Request to the server from the OpenAI client:

from openai import OpenAI
client = OpenAI(api_key="dummyapikey", base_url="http://127.0.0.1:8000/v1")
stream = client.chat.completions.create(
    model="roskosmos19/Rhea-4B-Coding",
    messages=[{
        "role": "user",
        "content": "Hey, write a Python function that determines if a string is a palindrome, ignoring case, spaces, and punctuation."
    }],
    max_tokens=16384,  # Increased for multi-pass output
    stream=True
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

vLLM Benefits: 20-30x faster inference, OpenAI-compatible API, continuous batching, async scheduling.

📝 Model Configuration

⚙️ Files Modified for Multi-Pass

🤝 Credits

• Base model: Aquiles-ai/Athenea-4B-Coding
• Dataset: Aquiles-ai/Athenea-Coding-100k
• Architecture: Qwen3 4B