File size: 2,673 Bytes
ba42040 dec60b4 ba42040 dec60b4 ba42040 dec60b4 ba42040 dec60b4 ba42040 dec60b4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
---
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))
``` |