Initial commit with draft readme and quantized models

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config.json

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+{
+    "model_type": "yi"
+}

readme.md

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+---
+base_model: 01-ai/Yi-6B
+inference: false
+license: other
+license_link: LICENSE
+license_name: yi-license
+model_creator: 01-ai
+model_name: Yi 6B
+model_type: yi
+prompt_template: 'Human: {prompt} Assistant:
+
+  '
+quantized_by: jezzarax
+---
+<!-- markdownlint-disable MD041 -->
+
+# Yi 6B - GGUF
+- Model creator: [01-ai](https://huggingface.co/01-ai)
+- Original model: [Yi 34B](https://huggingface.co/01-ai/Yi-6B)
+- Readme and repo format by [TheBloke](https://huggingface.co/TheBloke/) and his [Yi-34B-GGUF repo](https://huggingface.co/TheBloke/Yi-34B-GGUF)
+
+<!-- description start -->
+## Description
+
+This repo contains GGUF format model files for [01-ai's Yi 6B](https://huggingface.co/01-ai/Yi-6B).
+
+These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
+
+<!-- description end -->
+<!-- README_GGUF.md-about-gguf start -->
+### 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](https://github.com/ggerganov/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](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
+* [LM Studio](https://lmstudio.ai/), 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](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
+* [ctransformers](https://github.com/marella/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](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
+
+<!-- README_GGUF.md-about-gguf end -->
+<!-- repositories-available start -->
+## Repositories available
+
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/jezzarax/yi-6b-GGUF)
+* [01-ai's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/01-ai/Yi-6B)
+<!-- repositories-available end -->
+
+<!-- prompt-template start -->
+## Prompt template: Yi
+
+```
+Human: {prompt} Assistant:
+
+```
+
+<!-- prompt-template end -->
+
+
+<!-- compatibility_gguf start -->
+## Compatibility
+
+These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
+
+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
+
+<details>
+  <summary>Click to see details</summary>
+
+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.
+</details>
+<!-- compatibility_gguf end -->
+
+<!-- README_GGUF.md-provided-files start -->
+## Provided files
+
+| Name | Quant method | Bits | Size | Max RAM required | Use case |
+| ---- | ---- | ---- | ---- | ---- | ----- |
+| [yi-6b.Q2_K.gguf](https://huggingface.co/jezzarax/yi-6b-GGUF/blob/main/yi-6b.Q2_K.gguf) | Q2_K | 2 | 14.56 GB| 17.06 GB | smallest, significant quality loss - not recommended for most purposes |
+| [yi-6b.Q3_K_S.gguf](https://huggingface.co/jezzarax/yi-6b-GGUF/blob/main/yi-6b.Q3_K_S.gguf) | Q3_K_S | 3 | 14.96 GB| 17.46 GB | very small, high quality loss |
+| [yi-6b.Q3_K_M.gguf](https://huggingface.co/jezzarax/yi-6b-GGUF/blob/main/yi-6b.Q3_K_M.gguf) | Q3_K_M | 3 | 16.64 GB| 19.14 GB | very small, high quality loss |
+| [yi-6b.Q3_K_L.gguf](https://huggingface.co/jezzarax/yi-6b-GGUF/blob/main/yi-6b.Q3_K_L.gguf) | Q3_K_L | 3 | 18.14 GB| 20.64 GB | small, substantial quality loss |
+| [yi-6b.Q4_0.gguf](https://huggingface.co/jezzarax/yi-6b-GGUF/blob/main/yi-6b.Q4_0.gguf) | Q4_0 | 4 | 19.47 GB| 21.97 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [yi-6b.Q4_K_S.gguf](https://huggingface.co/jezzarax/yi-6b-GGUF/blob/main/yi-6b.Q4_K_S.gguf) | Q4_K_S | 4 | 19.54 GB| 22.04 GB | small, greater quality loss |
+| [yi-6b.Q4_K_M.gguf](https://huggingface.co/jezzarax/yi-6b-GGUF/blob/main/yi-6b.Q4_K_M.gguf) | Q4_K_M | 4 | 20.66 GB| 23.16 GB | medium, balanced quality - recommended |
+| [yi-6b.Q5_0.gguf](https://huggingface.co/jezzarax/yi-6b-GGUF/blob/main/yi-6b.Q5_0.gguf) | Q5_0 | 5 | 23.71 GB| 26.21 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [yi-6b.Q5_K_S.gguf](https://huggingface.co/jezzarax/yi-6b-GGUF/blob/main/yi-6b.Q5_K_S.gguf) | Q5_K_S | 5 | 23.71 GB| 26.21 GB | large, low quality loss - recommended |
+| [yi-6b.Q5_K_M.gguf](https://huggingface.co/jezzarax/yi-6b-GGUF/blob/main/yi-6b.Q5_K_M.gguf) | Q5_K_M | 5 | 24.32 GB| 26.82 GB | large, very low quality loss - recommended |
+| [yi-6b.Q6_K.gguf](https://huggingface.co/jezzarax/yi-6b-GGUF/blob/main/yi-6b.Q6_K.gguf) | Q6_K | 6 | 28.21 GB| 30.71 GB | very large, extremely low quality loss |
+| [yi-6b.Q8_0.gguf](https://huggingface.co/jezzarax/yi-6b-GGUF/blob/main/yi-6b.Q8_0.gguf) | Q8_0 | 8 | 36.54 GB| 39.04 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.
+
+
+
+<!-- README_GGUF.md-provided-files end -->
+
+<!-- README_GGUF.md-how-to-download start -->
+## 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:
+
+```shell
+pip3 install huggingface-hub
+```
+
+Then you can download any individual model file to the current directory, at high speed, with a command like this:
+
+```shell
+huggingface-cli download jezzarax/yi-6b-GGUF yi-6b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
+```
+
+<details>
+  <summary>More advanced huggingface-cli download usage</summary>
+
+You can also download multiple files at once with a pattern:
+
+```shell
+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](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
+
+To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
+
+```shell
+pip3 install hf_transfer
+```
+
+And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
+
+```shell
+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.
+</details>
+<!-- README_GGUF.md-how-to-download end -->
+
+<!-- README_GGUF.md-how-to-run start -->
+## Example `llama.cpp` command
+
+Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
+
+```shell
+./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](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
+
+## How to run in `text-generation-webui`
+
+Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
+
+## How to run from Python code
+
+You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/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:
+
+```shell
+# 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
+
+```python
+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:
+
+* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
+* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
+
+<!-- README_GGUF.md-how-to-run end -->
+
+<!-- original-model-card start -->
+# Original model card: 01-ai's Yi 6B
+
+<div align="center">
+
+<img src="./Yi.svg" width="200px">
+
+</div>
+
+## Introduction
+
+The **Yi** series models are large language models trained from scratch by
+developers at [01.AI](https://01.ai/). The first public release contains two
+bilingual(English/Chinese) base models with the parameter sizes of 6B([`Yi-6B`](https://huggingface.co/01-ai/Yi-6B))
+and 34B([`Yi-34B`](https://huggingface.co/01-ai/Yi-34B)). Both of them are trained
+with 4K sequence length and can be extended to 32K during inference time.
+The [`Yi-6B-200K`](https://huggingface.co/01-ai/Yi-6B-200K)
+and [`Yi-34B-200K`](https://huggingface.co/01-ai/Yi-34B-200K) are base model with
+200K context length.
+
+## News
+
+- 🎯 **2023/11/06**: The base model of [`Yi-6B-200K`](https://huggingface.co/01-ai/Yi-6B-200K)
+and [`Yi-34B-200K`](https://huggingface.co/01-ai/Yi-34B-200K) with 200K context length.
+- 🎯 **2023/11/02**: The base model of [`Yi-6B`](https://huggingface.co/01-ai/Yi-6B) and
+[`Yi-34B`](https://huggingface.co/01-ai/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](https://github.com/01-ai/Yi) 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](https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE). To
+apply for the official commercial license, please contact us
+([[email protected]](mailto:[email protected])).
+
+<!-- original-model-card end -->
+

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