Update README.md

README.md

@@ -9,14 +9,94 @@ tags:
 license: apache-2.0
 language:
 - en
+datasets:
+- LogicNet-Subnet/Aristole
 ---
 
-# Uploaded  model
+# Overview
 
-- **Developed by:** LogicNet-Subnet
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/qwen2-7b-instruct-bnb-4bit
+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:
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+- **Reliability**: 98.53%  
+- **Correctness**: 0.9739  
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+### 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))
+```