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--- |
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base_model: unsloth/qwen2-7b-instruct-bnb-4bit |
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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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- qwen2 |
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- trl |
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license: apache-2.0 |
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language: |
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- en |
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datasets: |
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- LogicNet-Subnet/Aristole |
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--- |
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# Overview |
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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: |
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- **Reliability**: 98.53% |
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- **Correctness**: 0.9739 |
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### Key Details: |
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- **Developed by**: LogicNet Team |
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- **License**: Apache 2.0 |
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- **Base Model**: [unsloth/qwen2-7b-instruct-bnb-4bit](https://huggingface.co/unsloth/qwen2-7b-instruct-bnb-4bit) |
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This fine-tuned Qwen2 model was trained **2x faster** using [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's **TRL** library. |
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--- |
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## Model and Training Hyperparameters |
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### Model Configuration: |
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- **dtype**: `torch.bfloat16` |
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- **load_in_4bit**: `True` |
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### Prompt Configuration: |
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- **max_seq_length**: `2048` |
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### PEFT Model Parameters: |
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- **r**: `16` |
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- **lora_alpha**: `16` |
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- **lora_dropout**: `0` |
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- **bias**: `"none"` |
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- **use_gradient_checkpointing**: `"unsloth"` |
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- **random_state**: `3407` |
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- **use_rslora**: `False` |
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- **loftq_config**: `None` |
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### Training Arguments: |
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- **per_device_train_batch_size**: `2` |
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- **gradient_accumulation_steps**: `4` |
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- **warmup_steps**: `5` |
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- **max_steps**: `70` |
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- **learning_rate**: `2e-4` |
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- **fp16**: `not is_bfloat16_supported()` |
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- **bf16**: `is_bfloat16_supported()` |
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- **logging_steps**: `1` |
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- **optim**: `"adamw_8bit"` |
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- **weight_decay**: `0.01` |
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- **lr_scheduler_type**: `"linear"` |
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- **seed**: `3407` |
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- **output_dir**: `"outputs"` |
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--- |
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## Training Results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.4764 | 1.0 | 1150 | 1.1850 | |
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| 1.3102 | 2.0 | 2050 | 1.1091 | |
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| 1.1571 | 3.0 | 3100 | 1.0813 | |
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| 1.0922 | 4.0 | 3970 | 0.9906 | |
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| 0.9809 | 5.0 | 5010 | 0.9021 | |
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## How To Use |
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You can easily use the model for inference as shown below: |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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# Load the model |
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tokenizer = AutoTokenizer.from_pretrained("LogicNet-Subnet/LogicNet-7B") |
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model = AutoModelForCausalLM.from_pretrained("LogicNet-Subnet/LogicNet-7B") |
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# Prepare the input |
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inputs = tokenizer( |
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[ |
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"what is odd which is bigger than zero?" # Example prompt |
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], |
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return_tensors="pt" |
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).to("cuda") |
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# Generate an output |
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outputs = model.generate(**inputs) |
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# Decode and print the result |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
