Triangle104/DRT-o1-7B-Q4_K_M-GGUF

This model was converted to GGUF format from Krystalan/DRT-o1-7B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

This repository contains the resources for our paper "DRT-o1: Optimized Deep Reasoning Translation via Long Chain-of-Thought"

Updates: 2024.12.24: We released our paper. Check it out! 2024.12.23: We released our model checkpoints. 🤗 DRT-o1-7B and 🤗 DRT-o1-14B. 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 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:

python3 -m vllm.entrypoints.openai.api_server --model [model_ckpt] --served-model-name [model_name]

Calling LLMs:

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

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/DRT-o1-7B-Q4_K_M-GGUF --hf-file drt-o1-7b-q4_k_m.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/DRT-o1-7B-Q4_K_M-GGUF --hf-file drt-o1-7b-q4_k_m.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/DRT-o1-7B-Q4_K_M-GGUF --hf-file drt-o1-7b-q4_k_m.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/DRT-o1-7B-Q4_K_M-GGUF --hf-file drt-o1-7b-q4_k_m.gguf -c 2048