Update README.md

README.md

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+# Cassandre-RAG
+
 Cassandre-RAG is a fine-tuned llama-3.1-8b model, built for RAG on French administrative documents, with a focus on sources from school administration.
 
 ## Training
 
 The model was trained on a H100, using these parameters:
 
-Training Hyperparameters
-
-Max Steps: 3000
-Learning Rate: 3e-4
-Batch Size: 2 per device
-Gradient Accumulation Steps: 4
-Max Sequence Length: 8192
-Weight Decay: 0.001
-Warmup Ratio: 0.03
-LR Scheduler: Linear
-Optimizer: paged_adamw_32bit
-
-LoRA Configuration
-
-LoRA Alpha: 16
-LoRA Dropout: 0.1
-LoRA R: 64
-Target Modules: ["gate_proj", "down_proj", "up_proj", "q_proj", "v_proj", "k_proj", "o_proj"]
-
-Quantization
-
-Quantization: 4-bit
-Quantization Type: nf4
-Compute Dtype: float16
+### Training Hyperparameters
+- Max Steps: 3000
+- Learning Rate: 3e-4
+- Batch Size: 2 per device
+- Gradient Accumulation Steps: 4
+- Max Sequence Length: 8192
+- Weight Decay: 0.001
+- Warmup Ratio: 0.03
+- LR Scheduler: Linear
+- Optimizer: paged_adamw_32bit
+
+### LoRA Configuration
+- LoRA Alpha: 16
+- LoRA Dropout: 0.1
+- LoRA R: 64
+- Target Modules: ["gate_proj", "down_proj", "up_proj", "q_proj", "v_proj", "k_proj", "o_proj"]
+
+### Quantization
+- Quantization: 4-bit
+- Quantization Type: nf4
+- Compute Dtype: float16
 
 ## Usage
 
 Cassandre-RAG uses a custom syntax for parsing sources and generating sourced output. 
-
 Each source should be preceded by an ID encapsulated in double asterisks (e.g., **SOURCE_ID**).
 
 ### Example Usage
 
+```python
 import pandas as pd
 from vllm import LLM, SamplingParams