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