metadata

base_model: 01-ai/Yi-6B
inference: false
model_creator: 01-ai
model_name: Yi 6B
model_type: yi
prompt_template: |
  Human: {prompt} Assistant:
quantized_by: jezzarax
license: apache-2.0

Yi 6B - GGUF

Model creator: 01-ai
Original model: Yi 34B
Readme and repo format by TheBloke and his Yi-34B-GGUF repo

Description

This repo contains GGUF format model files for 01-ai's Yi 6B.

These files were quantised using hardware kindly provided by Massed Compute.

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.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
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.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
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.

Repositories available

Prompt template: Yi

Human: {prompt} Assistant:

Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d

They are also compatible with many third party UIs and libraries - please see the list at the top of this README.

Explanation of quantisation methods

Click to see details

The new methods available are:

GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw

Refer to the Provided Files table below to see what files use which methods, and how.

Provided files

Name	Quant method	Bits	Size	Use case
yi-6b.Q2_K.gguf	Q2_K	2	2.5 GB	smallest, significant quality loss - not recommended for most purposes
yi-6b.Q3_K_S.gguf	Q3_K_S	3	2.6 GB	very small, high quality loss
yi-6b.Q3_K.gguf	Q3_K_M	3	2.8 GB	very small, high quality loss
yi-6b.Q3_K_L.gguf	Q3_K_L	3	3.1 GB	small, substantial quality loss
yi-6b.Q4_K_S.gguf	Q4_K_S	4	3.3 GB	small, greater quality loss
yi-6b.Q4_K.gguf	Q4_K	4	3.5 GB GB	medium, balanced quality - recommended
yi-6b.Q5_K_S.gguf	Q5_K_S	5	4.0 GB	large, low quality loss - recommended
yi-6b.Q5_K.gguf	Q5_K	5	4.1 GB	large, very low quality loss - recommended
yi-6b.Q6_K.gguf	Q6_K	6	4.7 GB	very large, extremely low quality loss
yi-6b.f16.gguf	f16	16	12 GB	very large, no quality loss - not recommended

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

How to download GGUF files

Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

LM Studio
LoLLMS Web UI
Faraday.dev

In `text-generation-webui`

Under Download Model, you can enter the model repo: jezzarax/yi-6b-GGUF and below it, a specific filename to download, such as: yi-6b.Q4_K_M.gguf.

Then click Download.

On the command line, including multiple files at once

I recommend using the huggingface-hub Python library:

pip3 install huggingface-hub

Then you can download any individual model file to the current directory, at high speed, with a command like this:

huggingface-cli download jezzarax/yi-6b-GGUF yi-6b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

More advanced huggingface-cli download usage

You can also download multiple files at once with a pattern:

huggingface-cli download jezzarax/yi-6b-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'

For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.

To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:

pip3 install hf_transfer

And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:

HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download jezzarax/yi-6b-GGUF yi-6b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False

Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.

Example `llama.cpp` command

Make sure you are using llama.cpp from commit d0cee0d or later.

./main -ngl 32 -m yi-6b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Human: {prompt} Assistant:"

Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change -c 2048 to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.

If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins

For other parameters and how to use them, please refer to the llama.cpp documentation

How to run in `text-generation-webui`

Further instructions here: text-generation-webui/docs/llama.cpp.md.

How to run from Python code

You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries.

How to load this model in Python code, using ctransformers

First install the package

Run one of the following commands, according to your system:

# Base ctransformers with no GPU acceleration
pip install ctransformers
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]
# Or with AMD ROCm GPU acceleration (Linux only)
CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems only
CT_METAL=1 pip install ctransformers --no-binary ctransformers

Simple ctransformers example code

from ctransformers import AutoModelForCausalLM

# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("jezzarax/yi-6b-GGUF", model_file="yi-6b.Q4_K_M.gguf", model_type="yi", gpu_layers=50)

print(llm("AI is going to"))

How to use with LangChain

Here are guides on using llama-cpp-python and ctransformers with LangChain:

Original model card: 01-ai's Yi 6B

Introduction

The Yi series models are large language models trained from scratch by developers at 01.AI. The first public release contains two bilingual(English/Chinese) base models with the parameter sizes of 6B(Yi-6B) and 34B(Yi-34B). Both of them are trained with 4K sequence length and can be extended to 32K during inference time. The Yi-6B-200K and Yi-34B-200K are base model with 200K context length.

News

🎯 2023/11/06: The base model of Yi-6B-200K and Yi-34B-200K with 200K context length.
🎯 2023/11/02: The base model of Yi-6B and Yi-34B.

Model Performance

Model	MMLU	CMMLU	C-Eval	GAOKAO	BBH	Common-sense Reasoning	Reading Comprehension	Math & Code
	5-shot	5-shot	5-shot	0-shot	3-shot@1	-	-	-
LLaMA2-34B	62.6	-	-	-	44.1	69.9	68.0	26.0
LLaMA2-70B	68.9	53.3	-	49.8	51.2	71.9	69.4	36.8
Baichuan2-13B	59.2	62.0	58.1	54.3	48.8	64.3	62.4	23.0
Qwen-14B	66.3	71.0	72.1	62.5	53.4	73.3	72.5	39.8
Skywork-13B	62.1	61.8	60.6	68.1	41.7	72.4	61.4	24.9
InternLM-20B	62.1	59.0	58.8	45.5	52.5	78.3	-	30.4
Aquila-34B	67.8	71.4	63.1	-	-	-	-	-
Falcon-180B	70.4	58.0	57.8	59.0	54.0	77.3	68.8	34.0
Yi-6B	63.2	75.5	72.0	72.2	42.8	72.3	68.7	19.8
Yi-6B-200K	64.0	75.3	73.5	73.9	42.0	72.0	69.1	19.0
Yi-34B	76.3	83.7	81.4	82.8	54.3	80.1	76.4	37.1
Yi-34B-200K	76.1	83.6	81.9	83.4	52.7	79.7	76.6	36.3

While benchmarking open-source models, we have observed a disparity between the results generated by our pipeline and those reported in public sources (e.g. OpenCompass). Upon conducting a more in-depth investigation of this difference, we have discovered that various models may employ different prompts, post-processing strategies, and sampling techniques, potentially resulting in significant variations in the outcomes. Our prompt and post-processing strategy remains consistent with the original benchmark, and greedy decoding is employed during evaluation without any post-processing for the generated content. For scores that were not reported by the original authors (including scores reported with different settings), we try to get results with our pipeline.

To evaluate the model's capability extensively, we adopted the methodology outlined in Llama2. Specifically, we included PIQA, SIQA, HellaSwag, WinoGrande, ARC, OBQA, and CSQA to assess common sense reasoning. SquAD, QuAC, and BoolQ were incorporated to evaluate reading comprehension. CSQA was exclusively tested using a 7-shot setup, while all other tests were conducted with a 0-shot configuration. Additionally, we introduced GSM8K (8-shot@1), MATH (4-shot@1), HumanEval (0-shot@1), and MBPP (3-shot@1) under the category "Math & Code". Due to technical constraints, we did not test Falcon-180 on QuAC and OBQA; the score is derived by averaging the scores on the remaining tasks. Since the scores for these two tasks are generally lower than the average, we believe that Falcon-180B's performance was not underestimated.

Usage

Please visit our github repository for general guidance on how to use this model.

Disclaimer

Although we use data compliance checking algorithms during the training process to ensure the compliance of the trained model to the best of our ability, due to the complexity of the data and the diversity of language model usage scenarios, we cannot guarantee that the model will generate correct and reasonable output in all scenarios. Please be aware that there is still a risk of the model producing problematic outputs. We will not be responsible for any risks and issues resulting from misuse, misguidance, illegal usage, and related misinformation, as well as any associated data security concerns.

License

The Yi series models are fully open for academic research and free commercial usage with permission via applications. All usage must adhere to the Model License Agreement 2.0. To apply for the official commercial license, please contact us ([email protected]).