Add README.md to repo

README.md

@@ -12,45 +12,46 @@ prompt_template: '<s>[INST] {prompt} [/INST]
 quantized_by: TheBloke
 tags:
 - finetuned
-- llamafile
 ---
 <!-- markdownlint-disable MD041 -->
 
 <!-- header start -->
 <!-- 200823 -->
 <div style="width: auto; margin-left: auto; margin-right: auto">
+<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
 </div>
 <div style="display: flex; justify-content: space-between; width: 100%;">
     <div style="display: flex; flex-direction: column; align-items: flex-start;">
-        <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/FwAVVu7eJ4">Chat & support: jartine's Discord server</a></p>
+        <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
     </div>
     <div style="display: flex; flex-direction: column; align-items: flex-end;">
+        <p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
     </div>
 </div>
-<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">jartine's LLM work is generously supported by a grant from <a href="https://mozilla.org">mozilla</a></p></div>
+<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
 <hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
 <!-- header end -->
 
-# Mistral 7B Instruct v0.2 - llamafile
+# Mistral 7B Instruct v0.2 - GGUF
 - Model creator: [Mistral AI_](https://huggingface.co/mistralai)
 - Original model: [Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
 
 <!-- description start -->
 ## Description
 
-This repo contains llamafile format model files for [Mistral AI_'s Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2).
+This repo contains GGUF format model files for [Mistral AI_'s Mistral 7B Instruct v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2).
 
 These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
 
-WARNING: This README may contain inaccuracies. It was generated automatically by forking <a href=/TheBloke/Mistral-7B-Instruct-v0.2-GGUF>TheBloke/Mistral-7B-Instruct-v0.2-GGUF</a> and piping the README through sed. Errors should be reported to jartine, and do not reflect TheBloke. You can support his work on [Patreon](https://www.patreon.com/TheBlokeAI).
-<!-- README_llamafile.md-about-llamafile start -->
-### About llamafile
+<!-- description end -->
+<!-- README_GGUF.md-about-gguf start -->
+### About GGUF
 
-llamafile is a new format introduced by Mozilla Ocho on Nov 20th 2023. It uses Cosmopolitan Libc to turn LLM weights into runnable llama.cpp binaries that run on the stock installs of six OSes for both ARM64 and AMD64.
+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 llamafile:
+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 llamafile. Offers a CLI and a server option.
+* [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.
 * [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
@@ -61,13 +62,13 @@ Here is an incomplete list of clients and libraries that are known to support ll
 * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
 * [ctransformers](https://github.com/marella/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.
 
-<!-- README_llamafile.md-about-llamafile end -->
+<!-- README_GGUF.md-about-gguf end -->
 <!-- repositories-available start -->
 ## Repositories available
 
-* [AWQ model(s) for GPU inference.](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-AWQ)
-* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-GPTQ)
-* [2, 3, 4, 5, 6 and 8-bit llamafile models for CPU+GPU inference](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile)
+* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-AWQ)
+* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF)
 * [Mistral AI_'s original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
 <!-- repositories-available end -->
 
@@ -82,10 +83,10 @@ Here is an incomplete list of clients and libraries that are known to support ll
 <!-- prompt-template end -->
 
 
-<!-- compatibility_llamafile start -->
+<!-- compatibility_gguf start -->
 ## Compatibility
 
-These quantised llamafilev2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
+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.
 
@@ -104,34 +105,34 @@ The new methods available are:
 
 Refer to the Provided Files table below to see what files use which methods, and how.
 </details>
-<!-- compatibility_llamafile end -->
+<!-- compatibility_gguf end -->
 
-<!-- README_llamafile.md-provided-files start -->
+<!-- README_GGUF.md-provided-files start -->
 ## Provided files
 
 | Name | Quant method | Bits | Size | Max RAM required | Use case |
 | ---- | ---- | ---- | ---- | ---- | ----- |
-| [mistral-7b-instruct-v0.2.Q2_K.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q2_K.llamafile) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
-| [mistral-7b-instruct-v0.2.Q3_K_S.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q3_K_S.llamafile) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
-| [mistral-7b-instruct-v0.2.Q3_K_M.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q3_K_M.llamafile) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
-| [mistral-7b-instruct-v0.2.Q3_K_L.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q3_K_L.llamafile) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
-| [mistral-7b-instruct-v0.2.Q4_0.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q4_0.llamafile) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
-| [mistral-7b-instruct-v0.2.Q4_K_S.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q4_K_S.llamafile) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
-| [mistral-7b-instruct-v0.2.Q4_K_M.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q4_K_M.llamafile) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
-| [mistral-7b-instruct-v0.2.Q5_0.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q5_0.llamafile) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
-| [mistral-7b-instruct-v0.2.Q5_K_S.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q5_K_S.llamafile) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
-| [mistral-7b-instruct-v0.2.Q5_K_M.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q5_K_M.llamafile) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
-| [mistral-7b-instruct-v0.2.Q6_K.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q6_K.llamafile) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
-| [mistral-7b-instruct-v0.2.Q8_0.llamafile](https://huggingface.co/jartine/Mistral-7B-Instruct-v0.2-llamafile/blob/main/mistral-7b-instruct-v0.2.Q8_0.llamafile) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended |
+| [mistral-7b-instruct-v0.2.Q2_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
+| [mistral-7b-instruct-v0.2.Q3_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
+| [mistral-7b-instruct-v0.2.Q3_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
+| [mistral-7b-instruct-v0.2.Q3_K_L.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
+| [mistral-7b-instruct-v0.2.Q4_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
+| [mistral-7b-instruct-v0.2.Q4_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
+| [mistral-7b-instruct-v0.2.Q4_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
+| [mistral-7b-instruct-v0.2.Q5_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
+| [mistral-7b-instruct-v0.2.Q5_K_S.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
+| [mistral-7b-instruct-v0.2.Q5_K_M.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
+| [mistral-7b-instruct-v0.2.Q6_K.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
+| [mistral-7b-instruct-v0.2.Q8_0.gguf](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GGUF/blob/main/mistral-7b-instruct-v0.2.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.
 
 
 
-<!-- README_llamafile.md-provided-files end -->
+<!-- README_GGUF.md-provided-files end -->
 
-<!-- README_llamafile.md-how-to-download start -->
-## How to download llamafile files
+<!-- 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.
 
@@ -143,7 +144,7 @@ The following clients/libraries will automatically download models for you, prov
 
 ### In `text-generation-webui`
 
-Under Download Model, you can enter the model repo: jartine/Mistral-7B-Instruct-v0.2-llamafile and below it, a specific filename to download, such as: mistral-7b-instruct-v0.2.Q4_K_M.llamafile.
+Under Download Model, you can enter the model repo: TheBloke/Mistral-7B-Instruct-v0.2-GGUF and below it, a specific filename to download, such as: mistral-7b-instruct-v0.2.Q4_K_M.gguf.
 
 Then click Download.
 
@@ -158,7 +159,7 @@ 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 jartine/Mistral-7B-Instruct-v0.2-llamafile mistral-7b-instruct-v0.2.Q4_K_M.llamafile --local-dir . --local-dir-use-symlinks False
+huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.2-GGUF mistral-7b-instruct-v0.2.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
 ```
 
 <details>
@@ -167,7 +168,7 @@ huggingface-cli download jartine/Mistral-7B-Instruct-v0.2-llamafile mistral-7b-i
 You can also download multiple files at once with a pattern:
 
 ```shell
-huggingface-cli download jartine/Mistral-7B-Instruct-v0.2-llamafile --local-dir . --local-dir-use-symlinks False --include='*Q4_K*llamafile'
+huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.2-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).
@@ -181,25 +182,25 @@ 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 jartine/Mistral-7B-Instruct-v0.2-llamafile mistral-7b-instruct-v0.2.Q4_K_M.llamafile --local-dir . --local-dir-use-symlinks False
+HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Mistral-7B-Instruct-v0.2-GGUF mistral-7b-instruct-v0.2.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_llamafile.md-how-to-download end -->
+<!-- README_GGUF.md-how-to-download end -->
 
-<!-- README_llamafile.md-how-to-run start -->
+<!-- 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 35 -m mistral-7b-instruct-v0.2.Q4_K_M.llamafile --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt} [/INST]"
+./main -ngl 35 -m mistral-7b-instruct-v0.2.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<s>[INST] {prompt} [/INST]"
 ```
 
 Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
 
-Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the llamafile file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
+Change `-c 32768` 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`
 
@@ -211,7 +212,7 @@ Further instructions can be found in the text-generation-webui documentation, he
 
 ## How to run from Python code
 
-You can use llamafile models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/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.
+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. 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
 
@@ -247,7 +248,7 @@ 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="./mistral-7b-instruct-v0.2.Q4_K_M.llamafile",  # Download the model file first
+  model_path="./mistral-7b-instruct-v0.2.Q4_K_M.gguf",  # Download the model file first
   n_ctx=32768,  # 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
@@ -263,7 +264,7 @@ output = llm(
 
 # Chat Completion API
 
-llm = Llama(model_path="./mistral-7b-instruct-v0.2.Q4_K_M.llamafile", chat_format="llama-2")  # Set chat_format according to the model you are using
+llm = Llama(model_path="./mistral-7b-instruct-v0.2.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."},
@@ -282,7 +283,7 @@ 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_llamafile.md-how-to-run end -->
+<!-- README_GGUF.md-how-to-run end -->
 
 <!-- footer start -->
 <!-- 200823 -->
@@ -290,23 +291,31 @@ Here are guides on using llama-cpp-python and ctransformers with LangChain:
 
 For further support, and discussions on these models and AI in general, join us at:
 
-[jartine AI's Discord server](https://discord.gg/FwAVVu7eJ4)
+[TheBloke AI's Discord server](https://discord.gg/theblokeai)
 
 ## Thanks, and how to contribute
 
+Thanks to the [chirper.ai](https://chirper.ai) team!
 
+Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
 
 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.
 
+* Patreon: https://patreon.com/TheBlokeAI
+* Ko-Fi: https://ko-fi.com/TheBlokeAI
 
+**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 mozilla for their generous grant.
+And thank you again to a16z for their generous grant.
 
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