Update README.md

README.md

@@ -1,11 +1,82 @@
 ---
-license: apache-2.0
+library_name: peft
+license: mit
+language:
+- en
+- it
+- fr
+datasets:
+- kaitchup/opus-Italian-to-English
+- kaitchup/opus-French-to-English
+tags:
+- translation
 ---
+# Model Card for Model ID
+
+This is an adapter for Meta's Llama 2 7B fine-tuned for translating Italian text into English. 
+## Model Details
 
 ### Model Description
 
-- **Developed by:** Bhuvnesh Siani
-- **Language(s) (NLP):** Python
-- **License:** [More Information Needed]
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** Bhuvnesh Saini
+- **Model type:** LoRA Adapter for Llama 2 7B
+- **Language(s) (NLP):** French, Italian, English
+- **License:** MIT license
+
+
+
+## Uses
+
+This adapter must be loaded on top of Llama 2 7B. It has been fine-tuned with QLoRA. For optimal results, the base model must be loaded with the exact same configuration used during fine-tuning.
+You can use the following code to load the model:
+```
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import torch
+from peft import PeftModel
+
+base_model = "meta-llama/Llama-2-7b-hf"
+compute_dtype = getattr(torch, "float16")
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=compute_dtype,
+    bnb_4bit_use_double_quant=True,
+)
+model = AutoModelForCausalLM.from_pretrained(
+        original_model_directory, device_map={"": 0}, quantization_config=bnb_config
+)
+tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True)
+model = PeftModel.from_pretrained(model, "kaitchup/Llama-2-7b-mt-Italian-to-English")
+```
+
+Then, run the model as follows:
+
+```
+my_text = "" #put your text to translate here
+
+prompt = my_text+" ###>"
+
+tokenized_input = tokenizer(prompt, return_tensors="pt")
+input_ids = tokenized_input["input_ids"].cuda()
+
+generation_output = model.generate(
+        input_ids=input_ids,
+        num_beams=10,
+        return_dict_in_generate=True,
+        output_scores=True,
+        max_new_tokens=130
+
+)
+for seq in generation_output.sequences:
+    output = tokenizer.decode(seq, skip_special_tokens=True)
+    print(output.split("###>")[1].strip()) 
+```
+
 
+## Model Card Contact
 
+[The Kaitchup](https://kaitchup.substack.com/)