Update README.md

README.md

@@ -12,4 +12,105 @@ tags:
 - medical
 - triage
 - emergency
----
+---
+
+# Llama-3.1-8B-Instruct-LoRA-SimSAMU
+
+This model is a fine-tuned version of **[meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)** using Low-Rank Adaptation (LoRA).
+
+It was specifically trained on the **[medkit/simsamu](https://huggingface.co/datasets/medkit/simsamu)** dataset to perform a specialized task: generating structured, task-oriented summaries from transcripts of emergency telephone calls to the French SAMU (Service d'Aide Médicale Urgente).
+
+The methodology and task are following the work presented in the **[QUARTZ](https://arxiv.org/abs/2509.26302)** paper.
+
+## Model Description
+
+-   **Base Model:** `meta-llama/Llama-3.1-8B-Instruct`
+-   **Dataset:** `medkit/simsamu`
+-   **Language:** French (`fr`)
+-   **Task:** The model takes a transcript of an emergency call and generates a Triage-oriented Structured Summary, extracting key information needed for medical triage and response.
+
+---
+
+## Intended Use
+
+This model is intended for research and development purposes in the field of medical NLP. Potential applications include:
+-   Assisting emergency call handlers by auto-generating summaries.
+-   Structuring unstructured call data for analysis.
+-   Training and simulation for medical dispatchers.
+
+**Note:** This model is a proof of concept and should **NOT** be used in a live clinical or emergency-response setting without extensive validation. 🚨
+
+---
+
+## How to Use
+
+You can use this model with the `transformers` library. Make sure you are logged into your Hugging Face account and have accepted the Llama 3.1 license terms.
+
+For more detailed examples, including the full prompts and code used, please visit the [**GitHub**](https://github.com/Mohamed-Imed-Eddine/QUARTZ) repository.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Ensure you have logged in to Hugging Face CLI
+# huggingface-cli login
+
+model_id = "Imed-Ghebriout/Llama-3.1-8B-Instruct-LoRA-SimSAMU"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# A shortened example transcript
+transcript = "medecin: bonjour docteur DETOURET au SAMU 93 vous appelez pour votre grand père c'est ça ?\npatient: oui c'est bien ça\nmedecin: d'accord vous êtes avec lui là ou pas ?\npatient: non non j'arrive là devant l'immeuble et je vois il y a de la fumée partout.\nmedecin: il y a des secours sur place monsieur ?\npatient: non non, il y a personne.\nmedecin: donc votre grand père il est à quel étage ?\npatient: il est au deuxième étage.\nmedecin: il peut se déplacer lui ou pas ?\npatient: bah je sais pas je suis pas encore rentré."
+
+# A shortened example of the system prompt
+system_prompt = """Vous êtes un médecin urgentiste. Votre tâche est de résumer le dialogue médical suivant sous la forme d’un compte rendu clinique précis et structuré.
+Format du compte rapport clinique:
+1-Motif principal de l’appel:
+2-Contexte de l’appel:
+3-Contexte du patient:
+4-Traitement habituel:
+5-Antécédents médicaux:
+6-Symptômes du patient:
+7-Histoire de la maladie actuelle:
+8-Hypothèses diagnostiques:
+9-Plan de traitement:
+10-Décision d’orientation:"""
+
+messages = [
+    {
+        "role": "system",
+        "content": system_prompt,
+    },
+    {
+        "role": "user",
+        "content": f"Dialogue médical:\n{transcript}\n---"
+    },
+]
+
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=512,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9,
+)
+
+response = outputs[0][input_ids.shape[-1]:]
+summary = tokenizer.decode(response, skip_special_tokens=True)
+print(summary)