Update README.md

README.md

@@ -5,7 +5,7 @@ datasets:
 language:
 - it
 ---
-# Model Card for Model ID
+# Model Card for itsrocchi/SeewebLLM-it-ver2
 
 <!-- Provide a quick summary of what the model is/does. -->
 
@@ -17,7 +17,8 @@ The model is a fine-tuned version of [LLama-2-7b-chat-hf](https://huggingface.co
 <!-- **Developed by:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
 - **Model type:** [More Information Needed] -->
-- **Language(s) (NLP):** Italian
+- **Backbone Model**: [LLama2](https://github.com/facebookresearch/llama/tree/main)
+- **Language(s) :** Italian
 - **Finetuned from model: [LLama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)**
 
 <!-- ### Model Sources [optional]
@@ -35,9 +36,45 @@ The model is a fine-tuned version of [LLama-2-7b-chat-hf](https://huggingface.co
 
 Due to a lack of training the model may not produce 100% correct output sentences.
 
+### Training script
 
-### Training Data
+The following repository contains scripts and instructions used for the finetuning and testing:
+
+**[https://github.com/itsrocchi/finetuning-llama2-ita.git](https://github.com/itsrocchi/finetuning-llama2-ita.git)**
+
+### Inference
+
+here's a little python snippet to perform inference
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+
+tokenizer = AutoTokenizer.from_pretrained("itsrocchi/SeewebLLM-it-ver2")
+model = AutoModelForCausalLM.from_pretrained(
+    "itsrocchi/SeewebLLM-it-ver2",
+    device_map="auto",
+    torch_dtype=torch.float16,
+    load_in_8bit=True,
+    rope_scaling={"type": "dynamic", "factor": 2} 
+)
+
+# eventualmente si possono modificare i parametri di model e tokenizer 
+# inserendo il percorso assoluto della directory locale del modello
+
+prompt = "### User:\nDescrivi cos' è l'intelligenza artificiale\n\n### Assistant:\n" 
+#modificare ciò che è scritto tra "User" ed "assistant per personalizzare il prompt" 
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+output = model.generate(**inputs, streamer=streamer, use_cache=True, max_new_tokens=float('inf'))
+output_text = tokenizer.decode(output[0], skip_special_tokens=True)
+```
+
+### Training Data and Details
 
 <!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-The dataset used is [itsrocchi/seeweb-it-292-forLLM](https://huggingface.co/datasets/itsrocchi/seeweb-it-292-forLLM), a dataset containing approx. 300 italian prompt-answer conversations.
+The dataset used is [itsrocchi/seeweb-it-292-forLLM](https://huggingface.co/datasets/itsrocchi/seeweb-it-292-forLLM), a dataset containing approx. 300 italian prompt-answer conversations.
+
+The training has been made on RTX A6000, inside [Seeweb's Cloud Server GPU](https://www.seeweb.it/prodotti/cloud-server-gpu)