Update README.md

README.md

@@ -5,4 +5,75 @@ language:
 library_name: adapter-transformers
 tags:
 - text-generation-inference
----
+---
+# finetune-indoMMLU-Merak-7B-v2
+Based on Merak-7B-v4 Mistral: https://huggingface.co/Ichsan2895/Merak-7B-v4<br>
+Dataset used on Fine Tuning: https://github.com/fajri91/IndoMMLU
+<br>
+
+Some training params used:
+```python
+lora r=64
+lora_alpha=16
+lora_dropout=0.05
+
+learning_rate = 2e-4
+lr_scheduler = "cosine"
+max_seq_length = 2048
+```
+
+Inference:
+```python
+import torch
+from transformers import AutoTokenizer, AutoConfig, AutoModelForCausalLM, BitsAndBytesConfig, LlamaTokenizer
+from peft import PeftModel, PeftConfig
+
+model_name = "Ichsan2895/Merak-7B-v4"
+adapter_name = "Willy030125/finetune-indoMMLU-Merak-7B-v2"
+
+bnb_config = transformers.BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+model = PeftModel.from_pretrained(model_name, adapter_name)
+tokenizer = LlamaTokenizer.from_pretrained(model_name)
+
+def generate_response(question: str) -> str:
+    chat = [
+      {"role": "system", "content": "Anda adalah Merak, sebuah model kecerdasan buatan yang dilatih oleh Muhammad Ichsan. Mohon jawab pertanyaan berikut dengan benar, faktual, dan ramah."},
+      {"role": "user", "content": question},
+    ]
+
+    prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+    inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=True)
+
+    with torch.no_grad():
+        outputs = model.generate(input_ids=inputs["input_ids"].to("cuda"),
+                           attention_mask=inputs.attention_mask,
+                           eos_token_id=tokenizer.eos_token_id,
+                           pad_token_id=tokenizer.eos_token_id,
+                           max_new_tokens=1024)
+        response = tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]
+
+        assistant_start = f'''{question} \n assistant\n '''
+        response_start = response.find(assistant_start)
+        return response[response_start + len(assistant_start) :].strip()
+
+prompt = """Hewan pemakan tumbuhan dinamakan ...
+A. Omnivora
+B. Karnivora
+C. Pengurai
+D. Herbivora"""
+
+print(generate_response(prompt))
+```