Example added

.gitignore

@@ -0,0 +1 @@
+/.idea/

README.md

@@ -19,4 +19,7 @@ tags:
 
 # Saiga/Yarn-Mistral 7B 128k, Russian Mistral-based chatbot
 
-Welcome to the adapter-only version of Saiga 7B LoRA. This model is built upon the foundation of [Nous-Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k).
+Welcome to the adapter-only version of Saiga 7B LoRA.
+This model is built upon the foundation of [Nous-Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k).
+
+

test_lora.py

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+import torch
+import logging
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+# MODEL_NAME = "IlyaGusev/gigasaiga_lora"
+# MODEL_NAME = "evilfreelancer/ruGPT-3.5-13B-lora"
+# MODEL_NAME = "./output"
+MODEL_NAME = "evilfreelancer/saiga_mistral_7b_128k_lora"
+DEFAULT_MESSAGE_TEMPLATE = "<s>{role}\n{content}</s>\n"
+DEFAULT_SYSTEM_PROMPT = """
+Ты — Saiga 2, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им.
+"""
+
+
+class Conversation:
+    def __init__(
+            self,
+            message_template=DEFAULT_MESSAGE_TEMPLATE,
+            system_prompt=DEFAULT_SYSTEM_PROMPT,
+            start_token_id=2,
+            # Bot token may be a list or single int
+            bot_token_id=10093,
+            # int (amount of questions and answers) or None (unlimited)
+            history_limit=None,
+    ):
+        self.logger = logging.getLogger('Conversation')
+        self.message_template = message_template
+        self.start_token_id = start_token_id
+        self.bot_token_id = bot_token_id
+        self.history_limit = history_limit
+        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_message(self, role, message):
+        self.messages.append({
+            "role": role,
+            "content": message
+        })
+        self.trim_history()
+
+    def add_user_message(self, message):
+        self.add_message("user", message)
+
+    def add_bot_message(self, message):
+        self.add_message("assistant", message)
+
+    def trim_history(self):
+        if self.history_limit is not None and len(self.messages) > self.history_limit + 1:
+            overflow = len(self.messages) - (self.history_limit + 1)
+            self.messages = [self.messages[0]] + self.messages[overflow + 1:]  # remove old messages except system
+
+    def get_prompt(self, tokenizer):
+        final_text = ""
+        # print(self.messages)
+        for message in self.messages:
+            message_text = self.message_template.format(**message)
+            final_text += message_text
+
+        # Bot token id may be an array
+        if isinstance(self.bot_token_id, (list, tuple)):
+            final_text += tokenizer.decode([self.start_token_id] + self.bot_token_id)
+        else:
+            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",
+    use_flash_attention_2=True,
+)
+model = PeftModel.from_pretrained(
+    model,
+    MODEL_NAME,
+    torch_dtype=torch.float16
+)
+model.eval()
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=False)
+generation_config = GenerationConfig.from_pretrained(MODEL_NAME)
+print(generation_config)
+
+template_path = 'internal_prompts/rugpt35.json'
+conversation = Conversation()
+while True:
+    user_message = input("User: ")
+
+    # Reset chat command
+    if user_message.strip() == "/reset":
+        conversation = Conversation()
+        print("History reset completed!")
+        continue
+
+    # Skip empty messages from user
+    if user_message.strip() == "":
+        continue
+
+    conversation.add_user_message(user_message)
+    prompt = conversation.get_prompt(tokenizer)
+    output = generate(
+        model=model,
+        tokenizer=tokenizer,
+        prompt=prompt,
+        generation_config=generation_config
+    )
+    conversation.add_bot_message(output)
+    print("Bot:", output)
+    print()
+    print("==============================")
+    print()