Create README.md

README.md

@@ -0,0 +1,46 @@
+---
+license: apache-2.0
+language:
+- ko
+pipeline_tag: text-generation
+---
+## Prompt Tempalte
+It follows Alpaca format.
+```
+### 질문: {instruction}
+### 답변: {output}
+```
+### Implementation Code
+```
+import troch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.fron_pretrained("Ja3ck/Mistral-instruct-IPO-Y24-v1", return_dict=True, torch_dtype=torch.bfloat16, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("Ja3ck/Mistral-instruct-IPO-Y24-v1", use_fast=True)
+tokenizer.pad_token = tokenizer.unk_token
+tokenizer.pad_token_id = tokenizer.unk_token_id
+tokenizer.padding_side = "left"
+
+def gen(x):
+  x_ = f"### 질문: {x.strip()} ### 답변: "
+  inputs = tokenizer(x_, return_tensor='pt')
+  input_ids = inputs['input_ids'].cuda()
+  generation_output = model.generate(
+      pad_token_id = tokenizer.pad_token_id,
+      temperature=0.1,
+      top_p=1,
+      top_k=50,
+      num_beams=1,
+      repetition_penalty=1.13,
+      do_sample=True,
+    ),
+    return_dict_in_generate=True,
+    output_scores=True,
+    max_new_tokens=1024
+  )
+  for seq in generation_output.sequences:
+    output = tokenizer.decode(seq)
+    print(output.split("### 답변: ")[1].strip())
+
+gen("안녕하세요?")
+```