Create README.md

README.md

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+---
+datasets:
+- IlyaGusev/gpt_roleplay_realm
+language:
+- en
+pipeline_tag: text-generation
+---
+
+LLaMA 7B fine-tuned on the `gpt_roleplay_realm` dataset.
+
+Code example:
+```
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+MODEL_NAME = "IlyaGusev/rpr_7b"
+DEFAULT_MESSAGE_TEMPLATE = "<s>{role}\n{content}</s>\n"
+
+class Conversation:
+    def __init__(
+        self,
+        system_prompt,
+        message_template=DEFAULT_MESSAGE_TEMPLATE,
+        start_token_id=1,
+        bot_token_id=9225
+    ):
+        self.message_template = message_template
+        self.start_token_id = start_token_id
+        self.bot_token_id = bot_token_id
+        self.messages = [{
+            "role": "system",
+            "content": system_prompt
+        }]
+
+    def get_start_token_id(self):
+        return self.start_token_id
+
+    def get_bot_token_id(self):
+        return self.bot_token_id
+
+    def add_user_message(self, message):
+        self.messages.append({
+            "role": "user",
+            "content": message
+        })
+
+    def add_bot_message(self, message):
+        self.messages.append({
+            "role": "bot",
+            "content": message
+        })
+
+    def get_prompt(self, tokenizer):
+        final_text = ""
+        for message in self.messages:
+            message_text = self.message_template.format(**message)
+            final_text += message_text
+        final_text += tokenizer.decode([self.start_token_id, self.bot_token_id])
+        return final_text.strip()
+
+
+def generate(model, tokenizer, prompt, generation_config):
+    data = tokenizer(prompt, return_tensors="pt")
+    data = {k: v.to(model.device) for k, v in data.items()}
+    output_ids = model.generate(**data,generation_config=generation_config)[0]
+    output_ids = output_ids[len(data["input_ids"][0]):]
+    output = tokenizer.decode(output_ids, skip_special_tokens=True)
+    return output.strip()
+
+
+config = PeftConfig.from_pretrained(MODEL_NAME)
+model = AutoModelForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    load_in_8bit=True,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+model = PeftModel.from_pretrained(
+    model,
+    MODEL_NAME,
+    torch_dtype=torch.float16
+)
+model.eval()
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+generation_config = GenerationConfig.from_pretrained(MODEL_NAME)
+print(generation_config)
+
+system_prompt = "You are Chiharu Yamada. Chiharu Yamada is a young, computer engineer-nerd with a knack for problem solving and a passion for technology."
+conversation = Conversation(system_prompt=system_prompt)
+for inp in inputs:
+    inp = input()
+    conversation.add_user_message(inp)
+    prompt = conversation.get_prompt(tokenizer)
+    output = generate(model, tokenizer, prompt, generation_config)
+    conversation.add_bot_message(output)
+    print(output)
+```