ertghiu256/Qwen3.5-2b-ReMix
🚀 Qwen3.5-2B-ReMix (Reasoning Mix) 🧠
This repository contains a fully merged, native Float16 (F16) fine-tune of Qwen/Qwen3.5-2B 🤖. The primary objective of this model is to significantly scale up performance on complex reasoning tasks, specifically targeting advanced mathematics 🧮, logical deduction, and structured coding problems 💻.
By leveraging multi-source open-source distillation data, it aims to achieve "frontier-style" reasoning capabilities while keeping the footprint compact enough to run smoothly at native speeds on local, everyday consumer hardware 🏠 without the need for external adapters.
🌟 Model Highlights
- 🏗️ Base Architecture: Qwen/Qwen3.5-2B (Dense, Hybrid Gated DeltaNet)
- 💾 Precision format: Native Float16 (F16) Merged Weights — No adapter required!
- 🎯 Main Goal: Advanced mathematical reasoning and complex code generation/debugging.
- 🛡️ Data Origin: 100% open-source distilled reasoning datasets natively hosted on Hugging Face. No proprietary data or closed APIs (OpenAI, Anthropic, Google) were used or involved in the collection or training process.
- ⚡ Target Environment: Local, high-efficiency edge execution with minimal hardware requirements.
🎛️ Recommended Generation Parameters
Depending on your use case, we recommend switching between "Everyday" and "Deep Reasoning" profiles to get the best performance out of the 2B architecture.
🏠 Everyday Use (Balanced)
🧠 Deep Reasoning
📊 Training & Merge Details
The model was adapted using Parameter-Efficient Fine-Tuning (PEFT) and then compiled back into the core network layers to output clean, unified F16 weights via Unsloth.
- 🔄 Training Steps: 175
- 📉 Loss Profile: Convergence floor reached ~0.58; stabilized consistently around 0.85
- 📈 Learning Rate:
4e-5 - 📐 LoRA Rank ($R$) during training:
16 - ⚖️ LoRA Alpha ($\alpha$) during training:
32
⚠️ Limitations & Risks
While this fine-tune aggressively pushes the boundaries of what a 2B parameter model can achieve locally, users should carefully account for the following behaviors:
- 🔮 Hallucinations: Like all highly compact models, it can confidently present false calculations or flawed code as absolute facts. Always verify outputs.
- 🎭 Inconsistent Styles: Due to the "ReMix" nature of the training data, the model may occasionally exhibit shifting output structures or stylistic variations.
- 🛑 Logic Mismatches: For extremely niche programming or high-level academic proofs, the model may occasionally produce broken syntax or reverse its logical assertions.
📦 How to Use Natively
🐍 Using Hugging Face Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_path = "YOUR_USERNAME/Qwen3.5-2B-ReMix"
# Load the aligned tokenizer and model weights directly
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto"
)
messages = [
{"role": "user", "content": "Explain the logic of a quicksort algorithm and implement it in Python."}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Using Reasoning Parameters (To not overthink)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=1024,
temperature=0.1,
top_k=60,
repeat_penalty=1.2
)Uploaded finetuned model
- Developed by: ertghiu256
- License: apache-2.0
- Finetuned from model : unsloth/Qwen3.5-2B
This qwen3_5 model was trained 2x faster with Unsloth and Huggingface's TRL library.