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TenyxChat-7B-v1-GGUF

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GGUF
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mistral
tenyx-fine-tuning
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tenyxchat
text-generation-inference
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TheBlokeAI

TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)

TenyxChat 7B v1 - GGUF

Description

This repo contains GGUF format model files for Tenyx's TenyxChat 7B v1.

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.
  • 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.

Repositories available

Prompt template: System-User-Assistant-nohash 

System: {system_message}
User: {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 Max RAM required Use case
tenyxchat-7b-v1.Q2_K.gguf Q2_K 2 2.70 GB 5.20 GB smallest, significant quality loss - not recommended for most purposes
tenyxchat-7b-v1.Q3_K_S.gguf Q3_K_S 3 3.16 GB 5.66 GB very small, high quality loss
tenyxchat-7b-v1.Q3_K_M.gguf Q3_K_M 3 3.52 GB 6.02 GB very small, high quality loss
tenyxchat-7b-v1.Q3_K_L.gguf Q3_K_L 3 3.82 GB 6.32 GB small, substantial quality loss
tenyxchat-7b-v1.Q4_0.gguf Q4_0 4 4.11 GB 6.61 GB legacy; small, very high quality loss - prefer using Q3_K_M
tenyxchat-7b-v1.Q4_K_S.gguf Q4_K_S 4 4.14 GB 6.64 GB small, greater quality loss
tenyxchat-7b-v1.Q4_K_M.gguf Q4_K_M 4 4.37 GB 6.87 GB medium, balanced quality - recommended
tenyxchat-7b-v1.Q5_0.gguf Q5_0 5 5.00 GB 7.50 GB legacy; medium, balanced quality - prefer using Q4_K_M
tenyxchat-7b-v1.Q5_K_S.gguf Q5_K_S 5 5.00 GB 7.50 GB large, low quality loss - recommended
tenyxchat-7b-v1.Q5_K_M.gguf Q5_K_M 5 5.13 GB 7.63 GB large, very low quality loss - recommended
tenyxchat-7b-v1.Q6_K.gguf Q6_K 6 5.94 GB 8.44 GB very large, extremely low quality loss
tenyxchat-7b-v1.Q8_0.gguf Q8_0 8 7.70 GB 10.20 GB very large, extremely low 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: TheBloke/TenyxChat-7B-v1-GGUF and below it, a specific filename to download, such as: tenyxchat-7b-v1.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 TheBloke/TenyxChat-7B-v1-GGUF tenyxchat-7b-v1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
More advanced huggingface-cli download usage (click to read)

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

huggingface-cli download TheBloke/TenyxChat-7B-v1-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 TheBloke/TenyxChat-7B-v1-GGUF tenyxchat-7b-v1.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 35 -m tenyxchat-7b-v1.Q4_K_M.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "System: {system_message}\nUser: {prompt}\nAssistant:"

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

Change -c 8192 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.

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 can be found in the text-generation-webui documentation, here: text-generation-webui/docs/04 ‐ Model Tab.md.

How to run from Python code

You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.

How to load this model in Python code, using llama-cpp-python

For full documentation, please see: llama-cpp-python docs.

First install the package

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

# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python

# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python

Simple llama-cpp-python example code 

from llama_cpp import Llama

# 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 = Llama(
  model_path="./tenyxchat-7b-v1.Q4_K_M.gguf",  # Download the model file first
  n_ctx=8192,  # The max sequence length to use - note that longer sequence lengths require much more resources
  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
)

# Simple inference example
output = llm(
  "System: {system_message}\nUser: {prompt}\nAssistant:", # Prompt
  max_tokens=512,  # Generate up to 512 tokens
  stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
  echo=True        # Whether to echo the prompt
)

# Chat Completion API

llm = Llama(model_path="./tenyxchat-7b-v1.Q4_K_M.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "You are a story writing assistant."},
        {
            "role": "user",
            "content": "Write a story about llamas."
        }
    ]
)

How to use with LangChain

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

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute

Thanks to the chirper.ai team!

Thanks to Clay from gpus.llm-utils.org!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

Special thanks to: Aemon Algiz.

Patreon special mentions: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: Tenyx's TenyxChat 7B v1

TenyxChat: Language Model Alignment using Tenyx Fine-tuning

Introducing TenyxChat, a series of ChatGPT-like models trained to function as useful assistants through preference tuning, using Tenyx's recently released advanced fine-tuning technology (VentureBeat article). Our first chat model in the series, TenyxChat-7B-v1, is trained using the Direct Preference Optimization (DPO) framework on the open-source AI feedback dataset UltraFeedback.

We fine-tune Openchat-3.5 with our proprietary approach (blog, service), which shows an increase in MT-Bench, without a drop in performance of the model on other benchmarks. Our approach aims to mitigate forgetting in LLMs in a computationally efficient manner, thereby enabling continual fine-tuning capabilities without altering the pre-trained output distribution. TenyxChat-7B-v1 was trained using eight A100s (80GB) for two hours, with a training setup obtained from HuggingFaceH4 (GitHub).

Model details

Usage

Our model uses a simple chat template based on OpenChat 3.5. The chat template usage with a Hugging face generation example is shown below.

Chat Template (Jinja) 

{{ bos_token }}
{% for message in messages %}
    {% if message['role'] == 'user' %}
        {{ 'User:' + message['content'] + eos_token }}

    {% elif message['role'] == 'system' %}
        {{ 'System:' + message['content'] + eos_token }}

    {% elif message['role'] == 'assistant' %}
        {{ 'Assistant:'  + message['content'] + eos_token }}

    {% endif %}

{% if loop.last and add_generation_prompt %}\n{{ 'Assistant:' }}{% endif %}\n{% endfor %}

Hugging face Example 

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="tenyx/TenyxChat-7B-v1", torch_dtype=torch.bfloat16, device_map="auto")

messages = [
    {"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate."},
    {"role": "user", "content": "Hi. I would like to make a hotel booking."},
]

prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=512, do_sample=False)

Output 

<s> System:You are a friendly chatbot who always responds in the style of a pirate.<|end_of_turn|>
User:Hi. I would like to make a hotel booking.<|end_of_turn|>
Assistant: Ahoy there me hearty! Arr, ye be lookin' fer a place to rest yer weary bones, eh?
Well then, let's set sail on this grand adventure and find ye a swell place to stay!

To begin, tell me the location ye be seekin' and the dates ye be lookin' to set sail.
And don't ye worry, me matey, I'll be sure to find ye a place that'll make ye feel like a king or queen on land!

Performance

At the time of release (Jan 2024), TenyxChat-7B-v1 is the highest-ranked 7B chat model on the MT-Bench evaluation available for download and commercial use. We list here the benchmark results on several standard setups while comparing popular 7B models as baselines.

MT-Bench

MT-Bench is a benchmark made up of 80 high-quality multi-turn questions. These questions fall into eight categories: Writing, Roleplay, Reasoning, Math, Coding, Extraction, STEM, and Humanities. The chat models are rated using GPT-4 on a scale of 1 to 10, with higher values corresponding to better responses.

Model First Turn Second Turn Average
GPT-4* 8.95625 9.02500 8.990625
TenyxChat-7B-v1 8.45000 7.75625 8.103125
Starling-lm-7B-alpha 8.42500 7.68750 8.056250
OpenChat-3.5 8.18125 7.41250 7.796875
GPT-3.5-turbo* 8.07500 7.81250 7.943750
OpenLLM Leader-7B** 8.05000 7.61250 7.831250

*values reported on lmsys ChatBot Arena

**The OpenLLM Leader as of Jan 5, 2024 is the merge model available as samir-fama/SamirGPT-v1

hexplot.png

Comparison with additional Open LLM LeaderBoard models

Model First Turn Second Turn Average
TenyxChat-7B-v1 8.45000 7.756250 8.103125
SamirGPT-v1 8.05000 7.612500 7.831250
FernandoGPT-v1 8.08125 7.256250 7.668750
Go-Bruins-v2 8.13750 7.150000 7.643750
mistral_tv-neural-marconroni 7.76875 6.987500 7.378125
neuronovo-7B-v0.2 7.73750 6.662500 7.200000
neural-chat-7b-v3-3 7.39375 5.881250 6.637500

LM Evaluation - Open LLM Leaderboard

We assess models on 7 benchmarks using the Eleuther AI Language Model Evaluation Harness. This setup is based of that used for Open LLM Leaderboard.

  • AI2 Reasoning Challenge (25-shot) - grade-school science questions.
  • HellaSwag (10-shot) - commonsense inference test.
  • MMLU (5-shot) - multitask accuracy test covering 57 tasks.
  • TruthfulQA (0-shot) - test measuring model's propensity to reproduce online falsehoods.
  • Winogrande (5-shot) - Winograd benchmark for commonsense reasoning.
  • GSM8k (5-shot) - grade school math word problems test.

These benchmarks test reasoning and knowledge in various tasks in few-shot settings (higher scores are better).

Model MMLU Winogrande GSM8k ARC HellaSwag TruthfulQA Average
TenyxChat-7B-v1 63.6 72.3 69.0 62.7 66.6 46.7 63.48
Starling-7B-alpha 63.5 72.1 67.9 61.1 66.1 42.1 62.13
OpenChat-3.5 63.6 72.1 68.2 61.3 65.2 41.8 62.03
Mistral-7B 62.4 74.0 38.1 57.2 62.8 37.8 55.38
OpenLLM Leader-7B 64.3 78.7 73.3 66.6 68.4 58.5 68.3

Note: While the Open LLM Leaderboard indicates that these chat models perform less effectively compared to the leading 7B model, it's important to note that the leading model struggles in the multi-turn chat setting of MT-Bench (as demonstrated in our evaluation above). In contrast, TenyxChat-7B-v1 demonstrates robustness against common fine-tuning challenges, such as catastrophic forgetting. This unique feature enables TenyxChat-7B-v1 to excel not only in chat benchmarks like MT-Bench, but also in a wider range of general reasoning benchmarks on the Open LLM Leaderboard.

Limitations

TenyxChat-7B-v1, like other small-sized language models, has its own set of limitations. We haven’t fine-tuned the model explicitly to align with human safety preferences. Therefore, it is capable of producing undesirable outputs, particularly when adversarially prompted. From our observation, the model still tends to struggle with tasks that involve reasoning and math questions. In some instances, it might generate verbose or extraneous content.

License

TenyxChat-7B-v1, similar to OpenChat 3.5, is distributed under the Apache License 2.0.

Citation

If you use TenyxChat-7B for your research, cite us as

@misc{tenyxchat2024,
      title={TenyxChat: Language Model Alignment using Tenyx Fine-tuning},
      author={Tenyx},
      year={2024},
}
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