File size: 2,790 Bytes
6280afa 43a2db4 6280afa 544eb07 6280afa 43a2db4 544eb07 6280afa 544eb07 43a2db4 544eb07 43a2db4 544eb07 6280afa 544eb07 43a2db4 544eb07 6280afa 544eb07 43a2db4 544eb07 6280afa 43a2db4 544eb07 43a2db4 6280afa |
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 |
# main.py — Gopu Agent-AI pour Friendli
import os
import torch
from transformers import PreTrainedTokenizerFast, GPTNeoXForCausalLM, GPTNeoXConfig
from huggingface_hub import login, upload_folder
# -------------------------------
# Paramètres du modèle (>2M)
# -------------------------------
model_config = {
"vocab_size": 50257,
"n_embd": 512,
"n_layer": 12,
"n_head": 8,
"block_size": 128,
"dropout": 0.1,
"model_type": "gpt_neox"
}
# -------------------------------
# Chemins de sauvegarde
# -------------------------------
paths = {
"model_save": "./agent_model",
"tokenizer_save": "./agent_tokenizer"
}
os.makedirs(paths["model_save"], exist_ok=True)
os.makedirs(paths["tokenizer_save"], exist_ok=True)
# -------------------------------
# Tokenizer minimal compatible Xenova + Friendli
# -------------------------------
tokenizer = PreTrainedTokenizerFast(
tokenizer_file=None,
bos_token="<s>",
eos_token="</s>",
unk_token="<unk>",
pad_token="<pad>"
)
tokenizer.save_pretrained(paths["tokenizer_save"])
# -------------------------------
# Création du modèle
# -------------------------------
config = GPTNeoXConfig(
vocab_size=model_config["vocab_size"],
hidden_size=model_config["n_embd"],
num_hidden_layers=model_config["n_layer"],
num_attention_heads=model_config["n_head"],
max_position_embeddings=model_config["block_size"],
dropout_rate=model_config["dropout"],
)
model = GPTNeoXForCausalLM(config)
# -------------------------------
# Sauvegarde en safetensors (recommandé pour Friendli)
# -------------------------------
model.save_pretrained(paths["model_save"], safe_serialization=True)
# -------------------------------
# Test rapide
# -------------------------------
prompt = "Bonjour Agent-AI, que peux-tu faire ?"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_length=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))
# -------------------------------
# Push vers Hugging Face Hub
# -------------------------------
HF_TOKEN = os.environ.get("HF_TOKEN")
REPO_ID = "Mauricio-100/agent-ai" # Change si tu veux publier ailleurs
if HF_TOKEN:
print("🔐 Connexion à Hugging Face Hub...")
login(token=HF_TOKEN)
print("📤 Upload du modèle...")
upload_folder(folder_path=paths["model_save"], repo_id=REPO_ID, repo_type="model")
upload_folder(folder_path=paths["tokenizer_save"], repo_id=REPO_ID, repo_type="model")
print(f"✅ Modèle poussé sur https://huggingface.co/{REPO_ID}")
print("🚀 Tu peux maintenant activer Friendli Endpoints depuis l’onglet [Deploy] du modèle.")
else:
print("⚠️ Aucun HF_TOKEN trouvé. Ajoute-le dans les Secrets ou les variables d’environnement.")
|