---
base_model: unsloth/qwen2-7b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
datasets:
- LogicNet-Subnet/Aristole
---

# Overview

This model is a fine-tuned version of **Qwen/Qwen2-7B-Instruct** on the **LogicNet-Subnet/Aristole** dataset. It achieves the following benchmarks on the evaluation set:

- **Reliability**: 98.53%  
- **Correctness**: 0.9739  

### Key Details:
- **Developed by**: LogicNet Team  
- **License**: Apache 2.0  
- **Base Model**: [unsloth/qwen2-7b-instruct-bnb-4bit](https://huggingface.co/unsloth/qwen2-7b-instruct-bnb-4bit)  

This fine-tuned Qwen2 model was trained **2x faster** using [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's **TRL** library.

---

## Model and Training Hyperparameters

### Model Configuration:
- **dtype**: `torch.bfloat16`  
- **load_in_4bit**: `True`

### Prompt Configuration:
- **max_seq_length**: `2048`

### PEFT Model Parameters:
- **r**: `16`  
- **lora_alpha**: `16`  
- **lora_dropout**: `0`  
- **bias**: `"none"`  
- **use_gradient_checkpointing**: `"unsloth"`  
- **random_state**: `3407`  
- **use_rslora**: `False`  
- **loftq_config**: `None`

### Training Arguments:
- **per_device_train_batch_size**: `2`  
- **gradient_accumulation_steps**: `4`  
- **warmup_steps**: `5`  
- **max_steps**: `70`  
- **learning_rate**: `2e-4`  
- **fp16**: `not is_bfloat16_supported()`  
- **bf16**: `is_bfloat16_supported()`  
- **logging_steps**: `1`  
- **optim**: `"adamw_8bit"`  
- **weight_decay**: `0.01`  
- **lr_scheduler_type**: `"linear"`  
- **seed**: `3407`  
- **output_dir**: `"outputs"`

---

## Training Results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4764        | 1.0   | 1150 | 1.1850          |
| 1.3102        | 2.0   | 2050 | 1.1091          |
| 1.1571        | 3.0   | 3100 | 1.0813          |
| 1.0922        | 4.0   | 3970 | 0.9906          |
| 0.9809        | 5.0   | 5010 | 0.9021          |


## How To Use
You can easily use the model for inference as shown below:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load the model
tokenizer = AutoTokenizer.from_pretrained("LogicNet-Subnet/LogicNet-7B")
model = AutoModelForCausalLM.from_pretrained("LogicNet-Subnet/LogicNet-7B")

# Prepare the input
inputs = tokenizer(
    [
        "what is odd which is bigger than zero?"  # Example prompt
    ],
    return_tensors="pt"
).to("cuda")

# Generate an output
outputs = model.generate(**inputs)

# Decode and print the result
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```