Update README.md

README.md

@@ -1,3 +1,54 @@
 ---
 license: openrail
+language:
+- it
+datasets:
+- teelinsan/camoscio
 ---
+
+# ExtremITA Camoscio 7 bilion parameters
+This is the base model trained on Italian instructions, a sibling of Alpaca.   
+It is based on [tellinsan/camoscio-7b-llama](https://huggingface.co/teelinsan/camoscio-7b-llama) adapters and the original LLaMA model, and it adds nothing new to [tellinsan/camoscio-7b-llama](https://huggingface.co/teelinsan/camoscio-7b-llama). Our version is the merged model with the adapters in order to obtain a more stable model that can be further fine-tuned, which we did for the [EVALITA 2023](https://www.evalita.it/campaigns/evalita-2023/) challenge.
+
+# Usage
+Checkout the github repository for more insights and codes: https://github.com/crux82/ExtremITA
+
+```python
+from transformers import LLaMATokenizer, LLaMAForCausalLM, GenerationConfig
+import torch
+
+tokenizer = LLaMATokenizer.from_pretrained("yahma/llama-7b-hf")
+model = LLaMAForCausalLM.from_pretrained(
+    "sag-uniroma2/extremITA-Camoscio-7b",
+    load_in_8bit=True,
+    device_map="auto",
+)
+
+generation_config = GenerationConfig(
+  temperature=0.2,
+  top_p=0.75,
+  top_k=40,
+  num_beams=4,
+)
+
+prompts = [
+  "Riassumi la storia di Pinocchio",
+  "Scrivi un programma che stampa i numeri da 1 a 100. Ma per i multipli di tre stampa 'Fizz' \
+  al posto del numero e per i multipli di cinque stampa 'Buzz'. Per i numeri che sono multipli \
+  sia di tre che di cinque stampa 'FizzBuzz'."
+]
+
+inputs = tokenizer(prompts, return_tensors="pt", padding=True, truncation=True).to(model.device)
+
+with torch.no_grad():
+  gen_outputs = model.generate(
+    **inputs,
+    generation_config=generation_config,
+    return_dict_in_generate=True,
+    output_scores=True,
+  )
+
+for i in range(len(gen_outputs)):
+  output = tokenizer.decode(gen_outputs[i], skip_special_tokens=True)
+  print(output)
+```