Update README.md

README.md

@@ -1,3 +1,79 @@
 ---
 license: cc-by-nc-sa-4.0
+language:
+- 'no'
 ---
+
+# Model Card
+
+NorGPT-3B-Instruction-peft is trained on top of [NorGPT-3B](https://huggingface.co/NorGLM/NorGPT-3B) model on [NO-Alpaca](https://huggingface.co/datasets/NbAiLab/norwegian-alpaca) dataset.
+
+Prompt format:
+```
+{instruction} {input} : {output}
+```
+
+Inference prompt:
+```
+{instruction} {input} :
+```
+
+## Run the Model
+```python
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+source_model_id = "NorGLM/NorGPT-3B"
+peft_model_id = "NorGLM/NorGPT-3B-Instruction-peft"
+
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced')
+
+tokenizer_max_len = 2048
+tokenizer_config = {'pretrained_model_name_or_path': source_model_id,
+                            'max_len': tokenizer_max_len}
+tokenizer = tokenizer = AutoTokenizer.from_pretrained(**tokenizer_config)
+tokenizer.pad_token = tokenizer.eos_token
+
+model = PeftModel.from_pretrained(model, peft_model_id)
+```
+
+## Inference Example
+Load the model to evaluate on the last 20\% of NO-Alpaca dataset:
+```python
+def merge_columns(example):
+    if str(example["input"]) == "":
+        example["text"] = str(example["instruction"]) + " : "
+    else:
+        example["text"] = str(example["instruction"]) + " " + str(example["input"]) + " : "
+    return example
+
+def generate_text(text, max_length=200, do_sample=True, top_p = 0.92, top_k=0):
+    set_seed(42)
+    model_inputs = tokenizer(text, return_tensors='pt').to(torch_device)
+    output = model.generate(**model_inputs, max_new_tokens = max_length, no_repeat_ngram_size=2, pad_token_id=tokenizer.eos_token_id)
+    return tokenizer.decode(output[0], skip_special_tokens=True)
+
+print("--LOADING EVAL DATAS---")
+eval_data = load_dataset("NbAiLab/norwegian-alpaca", split='train[-20%:]')
+
+print("--MAKING PREDICTIONS---")
+model.eval()
+
+output_file = <output file name>
+with open(output_file, 'w', encoding='utf-8-sig') as file:
+    generated_text = []
+    
+    for question in eval_data['text']:
+        generated_text.append({"generated_text": generate_text(question)})
+        print({"text_generated": len(generated_text)})
+
+    json_lines = [json.dumps(data) for data in generated_text]
+    json_data = "\n".join(json_lines)
+    file.write(json_data)
+
+```
+
+## Note
+More training details will be released soon!