| --- |
| 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)) |
| ``` |
