Update README.md

README.md

@@ -1,6 +1,18 @@
 ---
+language:
+- en
+library_name: transformers
+tags:
+- gpt
+- llm
+- large language model
+- h2o-llmstudio
 inference: false
-license: other
+thumbnail: >-
+  https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
+license: apache-2.0
+datasets:
+- OpenAssistant/oasst1
 ---
 
 <!-- header start -->
@@ -21,45 +33,47 @@ license: other
 
 These files are GGML format model files for [H2O's GPT-GM-OASST1-Falcon 40B v2](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2).
 
-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:
-* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
-* [KoboldCpp](https://github.com/LostRuins/koboldcpp)
-* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
-* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
-* [ctransformers](https://github.com/marella/ctransformers)
+These GGML files will **not** work in llama.cpp, text-generation-webui or KoboldCpp.
+
+They can be used from:
+* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui).
+* The ctransformers Python library, which includes LangChain support: [ctransformers](https://github.com/marella/ctransformers).
+* A new fork of llama.cpp that introduced this new Falcon GGML support: [cmp-nc/ggllm.cpp](https://github.com/cmp-nct/ggllm.cpp).
 
 ## Repositories available
 
 * [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GPTQ)
 * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2-GGML)
 * [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/h2oai/h2ogpt-gm-oasst1-en-2048-falcon-40b-v2)
-
+  
 <!-- compatibility_ggml start -->
 ## Compatibility
 
-### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`
+The recommended UI for these GGMLs is [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). Preliminary CUDA GPU acceleration is provided.
 
-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`.
+For use from Python code, use [ctransformers](https://github.com/marella/ctransformers). Again, with preliminary CUDA GPU acceleration
 
-These are guaranteed to be compatbile with any UIs, tools and libraries released since late May.
+Or to build cmp-nct's fork of llama.cpp with Falcon 7B support plus preliminary CUDA acceleration, please try the following steps:
 
-### 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`
+```
+git clone https://github.com/cmp-nct/ggllm.cpp
+cd ggllm.cpp
+rm -rf build && mkdir build && cd build && cmake -DGGML_CUBLAS=1 .. && cmake --build . --config Release
+```
 
-These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`.
+Compiling on Windows: developer cmp-nct notes: 'I personally compile it using VScode. When compiling with CUDA support using the Microsoft compiler it's essential to select the "Community edition build tools". Otherwise CUDA won't compile.'
 
-They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python and ctransformers. Other tools and libraries may or may not be compatible - check their documentation if in doubt.
+Once compiled you can then use `bin/falcon_main` just like you would use llama.cpp. For example:
+```
+bin/falcon_main -t 8 -ngl 100 -b 1 -m falcon7b-instruct.ggmlv3.q4_0.bin -p "What is a falcon?\n### Response:"
+```
+
+You can specify `-ngl 100` regardles of your VRAM, as it will automatically detect how much VRAM is available to be used. 
 
-## Explanation of the new k-quant methods
+Adjust `-t 8` (the number of CPU cores to use) according to what performs best on your system. Do not exceed the number of physical CPU cores you have.
 
-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
-* 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.
+`-b 1` reduces batch size to 1. This slightly lowers prompt evaluation time, but frees up VRAM to load more of the model on to your GPU.  If you find prompt evaluation too slow and have enough spare VRAM, you can remove this parameter.
 
-Refer to the Provided Files table below to see what files use which methods, and how.
 <!-- compatibility_ggml end -->
 
 ## Provided files
@@ -67,36 +81,17 @@ Refer to the Provided Files table below to see what files use which methods, and
 | ---- | ---- | ---- | ---- | ---- | ----- |
 | h2ogpt-falcon-40b.ggmlv3.q2_k.bin | q2_k | 2 | 13.74 GB | 16.24 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
 | h2ogpt-falcon-40b.ggmlv3.q3_k.bin | q3_k | 3 | 17.98 GB | 20.48 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
-| h2ogpt-falcon-40b.ggmlv3.q4_0.bin | q4_0 | 4 | 23.54 GB | 26.04 GB | Original llama.cpp quant method, 4-bit. |
-| h2ogpt-falcon-40b.ggmlv3.q4_1.bin | q4_1 | 4 | 26.16 GB | 28.66 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. |
+| h2ogpt-falcon-40b.ggmlv3.q4_0.bin | q4_0 | 4 | 23.54 GB | 26.04 GB | Old quant method, 4-bit. |
+| h2ogpt-falcon-40b.ggmlv3.q4_1.bin | q4_1 | 4 | 26.16 GB | 28.66 GB | Old quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
 | h2ogpt-falcon-40b.ggmlv3.q4_k.bin | q4_k | 4 | 23.54 GB | 26.04 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
-| h2ogpt-falcon-40b.ggmlv3.q5_0.bin | q5_0 | 5 | 28.77 GB | 31.27 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
-| h2ogpt-falcon-40b.ggmlv3.q5_1.bin | q5_1 | 5 | 31.38 GB | 33.88 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
+| h2ogpt-falcon-40b.ggmlv3.q5_0.bin | q5_0 | 5 | 28.77 GB | 31.27 GB | Old quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
+| h2ogpt-falcon-40b.ggmlv3.q5_1.bin | q5_1 | 5 | 31.38 GB | 33.88 GB | Old quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
 | h2ogpt-falcon-40b.ggmlv3.q5_k.bin | q5_k | 5 | 28.77 GB | 31.27 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
 | h2ogpt-falcon-40b.ggmlv3.q6_k.bin | q6_k | 6 | 34.33 GB | 36.83 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
-| h2ogpt-falcon-40b.ggmlv3.q8_0.bin | q8_0 | 8 | 44.46 GB | 46.96 GB | Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
+| h2ogpt-falcon-40b.ggmlv3.q8_0.bin | q8_0 | 8 | 44.46 GB | 46.96 GB | Old quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
 
 **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 run in `llama.cpp`
-
-I use the following command line; adjust for your tastes and needs:
-
-```
-./main -t 10 -ngl 32 -m h2ogpt-gm-oasst1-en-2048-falcon-40b0v2.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:"
-```
-If you're able to use full GPU offloading, you should use `-t 1` to get best performance.
-
-If not able to fully offload to GPU, you should use more cores. Change `-t 10` to the number of physical CPU cores you have, or a lower number depending on what gives best performance.
-
-Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
-
-If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
-
-## How to run in `text-generation-webui`
-
-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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