Update README.md

README.md

@@ -1,6 +1,77 @@
-Cassandre-RAG is a fine-tuned llama-3.1 model for RAG on administrative sources in France, especially in regards to school administration.
+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.
 
-## Use
-Cassandre-RAG relies on a custom syntax to parse sources and generate sourced output.
+## Training
 
-Each source has to be preceded by an id (can be anything) encapsulated into "**".
+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
+
+## 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
+
+import pandas as pd
+from vllm import LLM, SamplingParams
+
+# Load the model
+model_name = "PleIAs/Cassandre-RAG"
+llm = LLM(model_name, max_model_len=8128)
+
+# Set sampling parameters
+sampling_params = SamplingParams(
+    temperature=0.7,
+    top_p=0.95,
+    max_tokens=3000,
+    presence_penalty=1.2,
+    stop=["#END#"]
+)
+
+# Prepare the input data
+def prepare_prompt(query, sources):
+    sources_text = "\n\n".join([f"**{src_id}**\n{content}" for src_id, content in sources])
+    return f"### Query ###\n{query}\n\n### Source ###\n{sources_text}\n\n### Analysis ###\n"
+
+# Example query and sources
+query = "Quelles sont les procédures pour inscrire un enfant à l'école primaire?"
+sources = [
+    ("SOURCE_001", "L'inscription à l'école primaire se fait généralement à la mairie..."),
+    ("SOURCE_002", "Les documents nécessaires pour l'inscription scolaire incluent..."),
+]
+
+# Prepare the prompt
+prompt = prepare_prompt(query, sources)
+
+# Generate the response
+outputs = llm.generate([prompt], sampling_params)
+generated_text = outputs[0].outputs[0].text
+
+print("Query:", query)
+print("\nGenerated Response:")
+print(generated_text)