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Breeze-7B-Instruct-64k-v0.1-GGUF

Description

This repo contains GGUF format model files for MediaTek Research's Breeze-7B-Instruct-64k-v0.1.

About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

Here is an incomplete list of clients and libraries that are known to support GGUF:

  • llama.cpp. The source project for GGUF. Offers a CLI and a server option.
  • text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
  • KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
  • GPT4All, a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
  • LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
  • LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
  • Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
  • llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
  • candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
  • ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

Original model card

Breeze-7B is a language model family that builds on top of Mistral-7B, specifically intended for Traditional Chinese use.

Breeze-7B-Base is the base model for the Breeze-7B series. It is suitable for use if you have substantial fine-tuning data to tune it for your specific use case.

Breeze-7B-Instruct derives from the base model Breeze-7B-Base, making the resulting model amenable to be used as-is for commonly seen tasks.

Breeze-7B-Instruct-64k is a slightly modified version of Breeze-7B-Instruct to enable a 64k-token context length. Roughly speaking, that is equivalent to 88k Traditional Chinese characters.

The current release version of Breeze-7B is v0.1.

Practicality-wise:

  • Breeze-7B-Base expands the original vocabulary with additional 30,000 Traditional Chinese tokens. With the expanded vocabulary, everything else being equal, Breeze-7B operates at twice the inference speed for Traditional Chinese to Mistral-7B and Llama 7B. [See Inference Performance.]
  • Breeze-7B-Instruct can be used as is for common tasks such as Q&A, RAG, multi-round chat, and summarization.
  • In particular, Breeze-7B-Instruct-64k can perform tasks at a document level, not a chapter level.

Performance-wise:

  • Breeze-7B-Instruct demonstrates impressive performance in benchmarks for Traditional Chinese, when compared to similar sized open-source contemporaries such as Taiwan-LLM-7B/13B-chat, QWen-7B-Chat, and Yi-6B-Chat. [See Chat Model Performance.]
  • Breeze-7B-Instruct shows comparable results to Mistral-7B-Instruct-v0.1 on the MMLU and MT-Bench benchmarks. [See Chat Model Performance.]

A project by the members (in alphabetical order): Chan-Jan Hsu 許湛然, Chang-Le Liu 劉昶樂, Feng-Ting Liao 廖峰挺, Po-Chun Hsu 許博竣, Yi-Chang Chen 陳宜昌, and the supervisor Da-Shan Shiu 許大山.

Features

  • Breeze-7B-Base-v0.1
    • Expanding the vocabulary dictionary size from 32k to 62k to better support Traditional Chinese
    • 8k-token context length
  • Breeze-7B-Instruct-v0.1
    • Expanding the vocabulary dictionary size from 32k to 62k to better support Traditional Chinese
    • 8k-token context length
    • Multi-turn dialogue (without special handling for harmfulness)
  • Breeze-7B-Instruct-64k-v0.1
    • Expanding the vocabulary dictionary size from 32k to 62k to better support Traditional Chinese
    • 64k-token context length
    • Multi-turn dialogue (without special handling for harmfulness)

Model Details

  • Breeze-7B-Base-v0.1
    • Finetuned from: mistralai/Mistral-7B-v0.1
    • Model type: Causal decoder-only transformer language model
    • Language: English and Traditional Chinese (zh-tw)
  • Breeze-7B-Instruct-v0.1
  • Breeze-7B-Instruct-64k-v0.1

Base Model Performance

TMMLU+, DRCD, and Table source from MediaTek-Research/TCEval-v2. MediaTek-Research/TCEval-v2 derives from TCEval-v1 and ikala/tmmluplus. MMLU sources from hails/mmlu_no_train. We use the code revised from EleutherAI/lm-evaluation-harness to evaluate TMMLU+, DRCD, Table, and MMLU.

Models ↑ TMMLU+ (ACC) DRCD (EM) Table (ACC) MMLU (ACC)
TC, Knowledge TC, Reasoning TC, Reasoning EN, Knowledge
5 shot 3 shot 5 shot 5 shot
Yi-34B 34B 63.10 84.57 49.31 77.42
Qwen-14B 14B 51.30 16.95 * 50.69 68.83
Yi-6B 6B 49.63 76.61 34.72 65.35
Qwen-7B 7B 42.84 0.0 * 39.58 61.00
Breeze-7B-Base-v0.1 7B 40.35 81.13 28.47 61.63
Mistral-7B-v0.1 7B 36.93 79.27 27.78 64.89

* Few-shot learning cannot effectively guide the model to generate the proper answer.

Chat Model Performance

TMMLU+, DRCD, Table, and MT-Bench-tw source from MediaTek-Research/TCEval-v2. MediaTek-Research/TCEval-v2 derives from TCEval-v1 and ikala/tmmluplus. MMLU sources from hails/mmlu_no_train. MT-Bench source from lmsys/mt_bench_human_judgments. We use the code revised from EleutherAI/lm-evaluation-harness to evaluate TMMLU+, DRCD, Table, and MMLU. We use the code revised from fastchat llm_judge (GPT4 as judge) to evaluate MT-Bench-tw and MT-Bench.

Models ↑ MT-Bench-tw (Score) TMMLU+ (ACC) TMMLU+ (ACC) DRCD (EM) Table (ACC) MT-Bench (Score) MMLU (ACC) MMLU (ACC)
TC, Chat TC, Knowledge TC, Knowledge TC, Reasoning TC, Reasoning EN, Chat EN, Knowledge EN, Knowledge
0 shot 0 shot 5 shot 3 shot 0 shot 0 shot 0 shot 5 shot
gpt-3.5-turbo 7.1 41.76 7.9 70.00
Yi-34B-Chat 34B 6.9 54.87 36.81 7.6 71.04
Qwen-14B-Chat 14B 6.4 48.41 41.67 7.2 64.91
Breeze-7B-Instruct-v0.1 7B 5.7 41.61 45.83 7.1 63.26
Breeze-7B-Instruct-64k-v0.1 7B 5.5 40.99 36.11 7.1 63.68
Qwen-7B-Chat 7B 5.4 40.02 33.33 6.2 55.94
Yi-6B-Chat 6B 5.0 44.79 25.69 6.0 59.45
Taiwan-LLM-13B-v2.0-chat 13B 5.0 29.47 23.61 -* 50.50
Taiwan-LLM-7B-v2.1-chat 7B 4.2 28.08 31.25 -* 42.72

* Taiwan-LLM models responds to multi-turn questions (English) in Traditional Chinese.

Category Score of MT-Bench-tw (0 shot)

Models STEM Extraction Reasoning Math Coding Roleplay Writing Humanities ↑ AVG
gpt-3.5-turbo 7.8 6.1 5.1 6.4 6.2 8.7 7.4 9.3 7.1
Yi-34B-Chat 9.0 4.8 5.7 4.0 4.7 8.5 8.7 9.8 6.9
Qwen-14B-Chat 7.6 5.7 4.5 4.2 5.3 7.5 7.3 9.1 6.4
Breeze-7B-Instruct-v0.1 6.5 5.6 3.9 3.6 4.3 6.9 5.7 9.3 5.7
Breeze-7B-Instruct-64k-v0.1 6.1 5.3 3.7 2.9 4.2 7.0 6.7 8.3 5.5
Qwen-7B-Chat 6.6 4.5 4.8 2.9 3.6 6.2 6.8 8.2 5.4
Yi-6B-Chat 7.3 2.7 3.1 3.3 2.3 7.2 5.2 8.8 5.0
Taiwan-LLM-13B-v2.0-chat 6.1 3.4 4.1 2.3 3.1 7.4 6.6 6.8 5.0
Taiwan-LLM-7B-v2.1-chat 5.2 2.6 2.3 1.2 3.4 6.6 5.7 6.8 4.2

Category ACC of TMMLU+ (0 shot)

Model STEM Social Science Humanities Other ↑ AVG
Yi-34B-Chat 47.65 64.25 52.73 54.91 54.87
Qwen-14B-Chat 43.83 55.00 48.55 46.22 48.41
Yi-6B-Chat 37.80 51.74 45.36 44.25 44.79
gpt-3.5-turbo 41.56 46.72 36.73 42.03 41.76
Breeze-7B-Instruct-v0.1 37.41 46.81 42.06 40.16 41.61
Breeze-7B-Instruct-64k-v0.1 37.88 46.35 40.31 39.40 40.99
Qwen-7B-Chat 35.44 46.22 38.35 40.06 40.02
Taiwan-LLM-13B-v2.0-chat 27.74 33.69 27.03 29.43 29.47
Taiwan-LLM-7B-v2.1-chat 25.58 31.76 27.36 27.61 28.08

Inference Performance

In this test, we use the first 700 characters of the web article as the input and ask the model to write the same article again. All inferences run on 2 RTX A6000 GPUs (using vllm, with a tensor-parallel size of 2).

Models ↓ Inference Time (sec) Estimated Max Input Length (Char)
Yi-6B 10.62 5.2k
Breeze-7B-Instruct-v0.1 10.74 11.1k
Breeze-7B-Instruct-64k-v0.1 10.74 88.8k
Qwen-7B 10.86 9.8k
Qwen-14B 18.89 9.8k
Mistral-7B-v0.1 20.48 5.1k
Taiwan-LLM-7B-v2.1-base 26.26 2.2k
Taiwan-LLM-13B-v2.0-base 36.80 2.2k
Yi-34B 43.71 4.5k

Long-context Performance

TBD

Examples

TBD

Use in Transformers

First install direct dependencies:

pip install transformers torch accelerate

If you want faster inference using flash-attention2, you need to install these dependencies:

pip install packaging ninja
pip install flash-attn

Then load the model in transformers:

from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("MediaTek-Research/Breeze-7B-Instruct-v0.1")
tokenizer = AutoTokenizer.from_pretrained("MediaTek-Research/Breeze-7B-Instruct-v0.1")

# you can also using pipeline
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
generator(
    "請問台灣最高的山是",
    max_length=30,
    num_return_sequences=1,
)

The structure of the query template follows that of Mistral-7B-Instruct, as shown below.

<s> SYS_PROMPT   [INST] QUERY1 [/INST] RESPONSE1 [INST] QUERY2 [/INST]

where SYS_PROMPT, QUERY1, RESPONSE1, and QUERY2 can be provided by the user.

The suggested default SYS_PROMPT is

You are a helpful AI assistant built by MediaTek Research. The user you are helping speaks Traditional Chinese and comes from Taiwan.

Citation

@article{breeze7b2024,
  title={},
  author={},
  journal={arXiv},
  year={2024}
}
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