Causallm 7B DPO Alpha GGUF

🚀 CausalLM 7B-DPO-alpha - GGUF

This repository provides a quantized version of the CausalLM 7B-DPO-alpha model in GGUF format, enabling efficient text generation.

🚀 Quick Start

I created a quantized version of this model by referring to TheBloke's publishing format and based on the recommendation of TheBloke/CausalLM-7B-GGUF.

✨ Features

Model Information

• Model creator: CausalLM
• Original model: CausalLM 7B-DPO-alpha
• Model type: llama
• Pipeline tag: text-generation
• Prompt template:

<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

• Supported languages: en, zh
• License: wtfpl

Datasets

The model is trained on the following datasets:

• JosephusCheung/GuanacoDataset
• Open-Orca/OpenOrca
• stingning/ultrachat
• meta-math/MetaMathQA
• liuhaotian/LLaVA-Instruct-150K
• jondurbin/airoboros-3.1
• WizardLM/WizardLM_evol_instruct_V2_196k
• RyokoAI/ShareGPT52K
• RyokoAI/Fandom23K
• milashkaarshif/MoeGirlPedia_wikitext_raw_archive
• wikipedia
• wiki_lingua
• fnlp/moss-003-sft-data
• garage-bAInd/Open-Platypus
• LDJnr/Puffin
• openbmb/llava_zh
• BAAI/COIG
• TigerResearch/tigerbot-zhihu-zh-10k
• liwu/MNBVC
• teknium/openhermes

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.

Provided Files

📚 Documentation

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](https://github.com/oobabooga/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](https://github.com/ParisNeo/lollms - webui), 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](https://github.com/abetlen/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.

Licensing

The creator of the source model has listed its license as wtfpl, and this quantization has therefore used that same license.

As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: CausalLM's CausalLM 7B-DPO-alpha.

Explanation of Quantisation Methods

Original Model Card: CausalLM's CausalLM 7B-DPO-alpha

For details, please refer to the version without DPO training: CausalLM/7B.

It should be noted that this is not a version that continues training on CausalLM/14B & 7B, but rather an optimized version that has undergone DPO training concurrently on a previous training branch, and some detailed parameters may have changed. You will still need to download the full model.

The beta branch will soon be released, employing some aggressive approaches that might be detrimental in certain tasks, in order to achieve better alignment with human preferences, aiming to meet or exceed the GPT - 3.5 benchmarks. Stay tuned.

Disclaimer: Please note that the model was trained on unfiltered internet data. Since we do not have the capacity to vet all of it, there may be a substantial amount of objectionable content, pornography, violence, and offensive language present that we are unable to remove. Therefore, you will still need to complete your own checks on the model's safety and filter keywords in the output. Due to computational resource constraints, we are presently unable to implement RLHF for the model's ethics and safety, nor training on SFT samples that refuse to answer certain questions for restrictive fine - tuning.