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