Update README.md

README.md

@@ -10,4 +10,108 @@ tags:
 - O1-like model
 - Chat
 pipeline_tag: text-generation
----
+---
+
+# DRT-o1
+
+<p align="center">
+🤗 <a href="https://huggingface.co/Krystalan/DRT-o1-7B">DRT-o1-7B</a>&nbsp&nbsp | &nbsp&nbsp🤗 <a href="https://huggingface.co/Krystalan/DRT-o1-14B">DRT-o1-14B</a>&nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://arxiv.org/abs/2412.17498">Paper</a>
+
+</p>
+
+This repository contains the resources for our paper ["DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought"](https://arxiv.org/abs/2412.17498)
+
+
+### Updates:
+- *2024.12.24*: We released [our paper](https://arxiv.org/abs/2412.17498). Check it out!
+- *2024.12.23*: We released our model checkpoints. 🤗 <a href="https://huggingface.co/Krystalan/DRT-o1-7B">DRT-o1-7B</a> and 🤗 <a href="https://huggingface.co/Krystalan/DRT-o1-14B">DRT-o1-14B</a>.
+
+
+## Introduction
+
+In this work, we introduce DRT-o1, an attempt to bring the success of long thought reasoning to neural machine translation (MT). To this end,
+- 🌟 We mine English sentences with similes or metaphors from existing literature books, which are suitable for translation via long thought.
+- 🌟 We propose a designed multi-agent framework with three agents (i.e., a translator, an advisor and an evaluator) to synthesize the MT samples with long thought. There are 22,264 synthesized samples in total.
+- 🌟 We train DRT-o1-7B and DRT-o1-14B using Qwen2.5-7B-Instruct and Qwen2.5-14B-Instruct as backbones.
+
+
+
+## Quickstart
+
+### ⛷️ Huggingface Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Krystalan/DRT-o1-7B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Please translate the following text from English to Chinese:\nThe mother, with her feet propped up on a stool, seemed to be trying to get to the bottom of that answer, whose feminine profundity had struck her all of a heap."
+messages = [
+    {"role": "system", "content": "You are a philosopher skilled in deep thinking, accustomed to exploring complex problems with profound insight."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=2048
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+### ⛷️ vllm
+
+Deploying LLMs:
+```bash
+python3 -m vllm.entrypoints.openai.api_server --model [model_ckpt] --served-model-name [model_name]
+```
+
+Calling LLMs:
+```python
+from openai import OpenAI
+# Set OpenAI's API key and API base to use vLLM's API server.
+openai_api_key = "EMPTY"
+openai_api_base = "http://localhost:8000/v1"
+
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+chat_response = client.chat.completions.create(
+    model=[model_name],
+    messages=[
+        {"role": "system", "content": "You are a philosopher skilled in deep thinking, accustomed to exploring complex problems with profound insight."},
+        {"role": "user", "content": "Please translate the following text from English to Chinese:\nThe mother, with her feet propped up on a stool, seemed to be trying to get to the bottom of that answer, whose feminine profundity had struck her all of a heap."},
+    ],
+    temperature=0.7,
+    top_p=0.8,
+    max_tokens=2048,
+    extra_body={
+        "repetition_penalty": 1.05,
+    },
+)
+print("Chat response:", chat_response)
+```
+
+
+## License
+This work is licensed under cc-by-nc-sa-4.0
+