---
datasets:
- Aquiles-ai/Athenea-Coding-100k
- roskosmos19/Rhea-Coding
license: apache-2.0
tags:
- code
- agent
- merge
- uncensored
- Rhea
- multi-pass
- reasoning
language:
- en
pipeline_tag: text-generation
library_name: transformers
---
Rhea 4B Coding
**Rhea-4B-Coding** is an optimized version of [Aquiles-ai/Athenea-4B-Coding](https://huggingface.co/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 ``, ``, and `` 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](https://huggingface.co/datasets/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:
| Token | Purpose |
|-------|---------|
| `` | Start of self-review phase (Pass 2) |
| `` | End of self-review phase |
| `` | Start of review results documentation |
| `` | End of review results |
| `` | Start of final optimized version (Pass 3) |
| `` | End of final version |
This structure ensures **identical functionality** across all passes while improving **code structure, comments, and robustness**.
## 💻 Usage
### Installation
```bash
uv pip install transformers torch accelerate
```
### Basic Inference
```python
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)
```python
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<\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<\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
```python
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:**
```bash
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:**
```python
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
| Parameter | Value | Description |
|-----------|-------|-------------|
| `temperature` | 0.4 | Balanced creativity and consistency |
| `max_new_tokens` | 32768 | Full multi-pass output capacity |
| `repetition_penalty` | 1.0 | No penalty for intentional code repetition |
| `no_repeat_ngram_size` | 0 | Allows code structure repetition |
| `use_cache` | true | Faster inference for long outputs |
## ⚙️ Files Modified for Multi-Pass
| File | Changes |
|------|---------|
| `generation_config.json` | Extended tokens, optimized for multi-pass |
| `config.json` | Enabled caching, full context window |
| `tokenizer_config.json` | Added ``, ``, `` tokens |
| `special_tokens_map.json` | Registered new special tokens |
| `chat_template.jinja` | 3-pass prompt structure |
## 🤝 Credits
- Base model: [Aquiles-ai/Athenea-4B-Coding](https://huggingface.co/Aquiles-ai/Athenea-4B-Coding)
- Dataset: [Aquiles-ai/Athenea-Coding-100k](https://huggingface.co/datasets/Aquiles-ai/Athenea-Coding-100k)
- Architecture: Qwen3 4B
Roskosmos19