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Initial GGML model commit

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+ ---
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+ inference: false
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+ license: other
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+ ---
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+
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+ <!-- header start -->
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+ <div style="width: 100%;">
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+ <img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
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+ </div>
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+ <div style="display: flex; justify-content: space-between; width: 100%;">
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+ <div style="display: flex; flex-direction: column; align-items: flex-start;">
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+ <p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
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+ </div>
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+ <div style="display: flex; flex-direction: column; align-items: flex-end;">
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+ <p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
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+ </div>
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+ </div>
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+ <!-- header end -->
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+
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+ # Camel AI's CAMEL 13B Role Playing Data GGML
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+
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+ These files are GGML format model files for [Camel AI's CAMEL 13B Role Playing Data](https://huggingface.co/camel-ai/CAMEL-13B-Role-Playing-Data).
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+
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+ GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
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+ * [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
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+ * [KoboldCpp](https://github.com/LostRuins/koboldcpp)
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+ * [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui)
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+ * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
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+ * [ctransformers](https://github.com/marella/ctransformers)
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+
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+ ## Repositories available
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+
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+ * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/CAMEL-13B-Role-Playing-Data-GPTQ)
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+ * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/CAMEL-13B-Role-Playing-Data-GGML)
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+ * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/CAMEL-13B-Role-Playing-Data-fp16)
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+
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+ <!-- compatibility_ggml start -->
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+ ## Compatibility
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+
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+ ### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`
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+
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+ I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`.
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+
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+ They should be compatible with all current UIs and libraries that use llama.cpp, such as those listed at the top of this README.
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+
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+ ### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`
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+
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+ These new quantisation methods are only compatible with llama.cpp as of June 6th, commit `2d43387`.
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+ They will NOT be compatible with koboldcpp, text-generation-ui, and other UIs and libraries yet. Support is expected to come over the next few days.
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+
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+ ## Explanation of the new k-quant methods
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+
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+ The new methods available are:
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+ * 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)
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+ * 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.
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+ * 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.
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+ * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
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+ * 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
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+ * GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.
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+
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+ Refer to the Provided Files table below to see what files use which methods, and how.
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+ <!-- compatibility_ggml end -->
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+
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+ ## Provided files
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+ | Name | Quant method | Bits | Size | Max RAM required | Use case |
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+ | ---- | ---- | ---- | ---- | ---- | ----- |
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+ | camel-13b-roleplay.ggmlv3.q2_K.bin | q2_K | 2 | 5.43 GB | 7.93 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
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+ | camel-13b-roleplay.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 6.87 GB | 9.37 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
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+ | camel-13b-roleplay.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 6.25 GB | 8.75 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
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+ | camel-13b-roleplay.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 5.59 GB | 8.09 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
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+ | camel-13b-roleplay.ggmlv3.q4_0.bin | q4_0 | 4 | 7.32 GB | 9.82 GB | Original llama.cpp quant method, 4-bit. |
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+ | camel-13b-roleplay.ggmlv3.q4_1.bin | q4_1 | 4 | 8.14 GB | 10.64 GB | Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
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+ | camel-13b-roleplay.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 7.82 GB | 10.32 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
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+ | camel-13b-roleplay.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 7.32 GB | 9.82 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
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+ | camel-13b-roleplay.ggmlv3.q5_0.bin | q5_0 | 5 | 8.95 GB | 11.45 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
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+ | camel-13b-roleplay.ggmlv3.q5_1.bin | q5_1 | 5 | 9.76 GB | 12.26 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
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+ | camel-13b-roleplay.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 9.21 GB | 11.71 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
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+ | camel-13b-roleplay.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 8.95 GB | 11.45 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
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+ | camel-13b-roleplay.ggmlv3.q6_K.bin | q6_K | 6 | 10.68 GB | 13.18 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
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+ | camel-13b-roleplay.ggmlv3.q8_0.bin | q8_0 | 8 | 13.83 GB | 16.33 GB | Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
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+
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+ **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.
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+
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+ ## How to run in `llama.cpp`
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+
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+ I use the following command line; adjust for your tastes and needs:
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+
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+ ```
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+ ./main -t 10 -ngl 32 -m camel-13b-roleplay.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
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+ ```
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+ Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.
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+ Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
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+ If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
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+ ## How to run in `text-generation-webui`
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+ Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
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+ <!-- footer start -->
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+ ## Discord
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+ For further support, and discussions on these models and AI in general, join us at:
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+ [TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
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+
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+ ## Thanks, and how to contribute.
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+ Thanks to the [chirper.ai](https://chirper.ai) team!
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+ 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.
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+ 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.
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+ Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
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+ * Patreon: https://patreon.com/TheBlokeAI
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+ * Ko-Fi: https://ko-fi.com/TheBlokeAI
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+ **Special thanks to**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
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+ **Patreon special mentions**: Ajan Kanaga, Kalila, Derek Yates, Sean Connelly, Luke, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, trip7s trip, Jonathan Leane, Talal Aujan, Artur Olbinski, Cory Kujawski, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Johann-Peter Hartmann.
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+ Thank you to all my generous patrons and donaters!
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+ <!-- footer end -->
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+ # Original model card: Camel AI's CAMEL 13B Role Playing Data
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+ CAMEL-13B-Role-Playing-Data is a chat large language model obtained by finetuning LLaMA-13B model on a total of 229K conversations created through our role-playing framework proposed in [CAMEL](https://arxiv.org/abs/2303.17760). We evaluate our model offline using EleutherAI's language model evaluation harness used by Huggingface's Open LLM Benchmark. CAMEL-13B scores an average of **57.2**, outperfroming LLaMA-30B (56.9)!
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+ | Model | size | ARC-C (25 shots, acc_norm) | HellaSwag (10 shots, acc_norm) | MMLU (5 shots, acc_norm) | TruthfulQA (0 shot, mc2) | Average | Delta |
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+ |-------------|:----:|:---------------------------:|:-------------------------------:|:-------------------------:|:-------------------------:|:-------:|-------|
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+ | LLaMA | 13B | 50.8 | 78.9 | 37.7 | 39.9 | 51.8 | - |
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+ | Vicuna | 13B | 47.4 | 75.2 | 39.6 | 49.8 | 53.7 | 1.9 |
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+ | CAMEL | 13B | 54.9 | 79.3 | 48.5 | 46.2 | **57.2** | 5.4 |
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+ | LLaMA | 30B | 57.1 | 82.6 | 45.7 | 42.3 | 56.9 | 5.1 |
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+
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+ ---
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+ license: cc-by-nc-4.0
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+ ---