初始化项目，由ModelHub XC社区提供模型

Model: Vikhrmodels/Vikhr-7B-instruct_0.2
Source: Original Platform

README.md

@@ -0,0 +1,105 @@
+---
+language:
+- ru
+- en
+datasets:
+- zjkarina/Vikhr_instruct
+- dichspace/darulm
+---
+
+GGUF версия: https://huggingface.co/pirbis/Vikhr-7B-instruct_0.2-GGUF
+
+```python
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+import torch
+import os
+os.environ['HF_HOME']='.'
+MODEL_NAME = "Vikhrmodels/Vikhr-7B-instruct_0.2"
+DEFAULT_MESSAGE_TEMPLATE = "<s>{role}\n{content}</s>\n"
+DEFAULT_SYSTEM_PROMPT = "Ты — Вихрь, 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,
+    ):
+        self.message_template = message_template
+        self.messages = [{
+            "role": "system",
+            "content": system_prompt
+        }]
+
+    def add_user_message(self, message):
+        self.messages.append({
+            "role": "user",
+            "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 += 'bot'
+        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(
+    MODEL_NAME,
+    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, use_fast=False)
+
+generation_config = GenerationConfig.from_pretrained(MODEL_NAME)
+generation_config.max_length=256
+generation_config.top_p=0.9
+generation_config.top_k=30
+generation_config.do_sample = True
+print(generation_config)
+
+inputs = ["Как тебя зовут?", "Кто такой Колмогоров?"]
+
+for inp in inputs:
+    conversation = Conversation()
+    conversation.add_user_message(inp)
+    prompt = conversation.get_prompt(tokenizer)
+
+    output = generate(model, tokenizer, prompt, generation_config)
+    print(inp)
+    print(output)
+    print('\n')
+```
+
+[wandb](https://wandb.ai/karina_romanova/vikhr/runs/up2hw5eh?workspace=user-karina_romanova)
+
+
+## Cite
+```
+@inproceedings{nikolich2024vikhr,
+  title={Vikhr: Constructing a State-of-the-art Bilingual Open-Source Instruction-Following Large Language Model for {Russian}},
+  author={Aleksandr Nikolich and Konstantin Korolev and Sergei Bratchikov and  Igor Kiselev and Artem Shelmanov },
+  booktitle = {Proceedings of the 4rd Workshop on Multilingual Representation Learning (MRL) @ EMNLP-2024}
+  year={2024},
+  publisher = {Association for Computational Linguistics},
+  url={https://arxiv.org/pdf/2405.13929}
+}
+```