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---
base_model: Qwen/Qwen3-32B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- fast-reasoning
- efficient-llm
license: apache-2.0
language:
- en
library_name: transformers
---
# 🔥 Phoenix — Fast Reasoning Qwen3-32B
**Model Name:** `Daemontatox/Phoenix`
**Developed by:** `Daemontatox`
**License:** `Apache-2.0`
**Base Model:** [`unsloth/qwen3-32b`](https://huggingface.co/unsloth/qwen3-32b)
**Training Stack:** [Unsloth](https://github.com/unslothai/unsloth) + Huggingface [`TRL`](https://github.com/huggingface/trl)
---
## ⚡ What is Phoenix?
**Phoenix** is a finetuned Qwen3-32B model designed for **rapid reasoning**, **low-token verbosity**, and **high-quality results**. Ideal for chat agents, reasoning backends, and any application where **speed and precision** are critical.
---
## ✅ Key Features
- 🔁 **2× faster training** with Unsloth
- ⏱️ **Reduced token latency** without compromising answer quality
- 🎯 Tuned for **instruction-following and reasoning clarity**
- 🧱 Works with `transformers`, `TGI`, and `Hugging Face Inference API`
---
## 🧪 Inference Code (Transformers)
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name = "Daemontatox/Phoenix"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True
)
prompt = "Explain the concept of emergence in complex systems in simple terms."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=150, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
---
🌐 Inference via Hugging Face API
```python
import requests
API_URL = "https://api-inference.huggingface.co/models/Daemontatox/Phoenix"
headers = {"Authorization": "Bearer YOUR_HF_API_TOKEN"}
data = {
"inputs": "Explain the concept of emergence in complex systems in simple terms.",
"parameters": {
"temperature": 0.7,
"max_new_tokens": 150
}
}
```
response = requests.post(API_URL, headers=headers, json=data)
print(response.json()[0]["generated_text"])
> ⚠️ Replace YOUR_HF_API_TOKEN with your Hugging Face access token.
---
🧠 Sample Output
Prompt:
> "Explain the concept of emergence in complex systems in simple terms."
Output (Phoenix):
> "Emergence is when many simple parts work together and create something more complex. For example, birds flying in a flock follow simple rules, but the group moves like one unit. That larger pattern 'emerges' from simple behavior."
---
📉 Known Limitations
Large VRAM required for local inference (~64GB+)
Not tuned for multilingual inputs
May not perform well on long-form CoT problems requiring step-wise thought
---
📄 Citation
@misc{daemontatox2025phoenix,
title={Phoenix: Fast Reasoning Qwen3-32B Finetune},
author={Daemontatox},
year={2025},
note={Trained with Unsloth and Huggingface TRL},
url={https://huggingface.co/Daemontatox/Phoenix}
}
---
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