Update README.md

README.md

@@ -8,101 +8,69 @@ extra_gated_prompt: >-
   Google’s usage license. To do this, please ensure you’re logged in to Hugging
   Face and click below. Requests are processed immediately.
 extra_gated_button_content: Acknowledge license
+tags:
+- conversational
 quantized_by: bartowski
+lm_studio:
+  param_count: 27b
+  use_case: general
+  release_date: 27-06-2024
+  model_creator: google
+  prompt_template: Google Gemma Instruct
+  system_prompt: none
+  base_model: gemma
+  original_repo: google/gemma-2-27b-it
+base_model: google/gemma-2-27b-it
 ---
 
-## Llamacpp imatrix Quantizations of gemma-2-27b-it
+## 💫 Community Model> Gemma 2 27b Instruct by Google
 
-Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3259">b3259</a> for quantization.
+*👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*.
 
-Original model: https://huggingface.co/google/gemma-2-27b-it
+**Model creator:** [Google](https://huggingface.co/google)<br>
+**Original model**: [gemma-2-27b-it](https://huggingface.co/google/gemma-2-27b-it)<br>
+**GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` PR [8156](https://github.com/ggerganov/llama.cpp/pull/8156)<br>
 
-All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
+## Model Settings:
 
-## Prompt format
+Requires LM Studio 0.2.26, update can be downloaded from here: https://lmstudio.ai
 
-```
-<bos><start_of_turn>user
-{prompt}<end_of_turn>
-<start_of_turn>model
-<end_of_turn>
-<start_of_turn>model
-
-```
-
-Note that this model does not support a System prompt.
-
-## Download a file (not the whole branch) from below:
-
-| Filename | Quant type | File Size | Description |
-| -------- | ---------- | --------- | ----------- |
-| [gemma-2-27b-it-Q8_0_L.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-Q8_1.gguf) | Q8_0_L | 30.04GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Extremely high quality, generally unneeded but max available quant. |
-| [gemma-2-27b-it-Q8_0.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-Q8_0.gguf) | Q8_0 | 28.93GB | Extremely high quality, generally unneeded but max available quant. |
-| [gemma-2-27b-it-Q6_K_L.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-Q6_K_L.gguf) | Q6_K_L | 23.73GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Very high quality, near perfect, *recommended*. |
-| [gemma-2-27b-it-Q6_K.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-Q6_K.gguf) | Q6_K | 22.34GB | Very high quality, near perfect, *recommended*. |
-| [gemma-2-27b-it-Q5_K_L.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-Q5_K_L.gguf) | Q5_K_L | 20.79GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. High quality, *recommended*. |
-| [gemma-2-27b-it-Q5_K_M.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-Q5_K_M.gguf) | Q5_K_M | 19.40GB | High quality, *recommended*. |
-| [gemma-2-27b-it-Q5_K_S.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-Q5_K_S.gguf) | Q5_K_S | 18.88GB | High quality, *recommended*. |
-| [gemma-2-27b-it-Q4_K_L.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-Q4_K_L.gguf) | Q4_K_L | 18.03GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Good quality, uses about 4.83 bits per weight, *recommended*. |
-| [gemma-2-27b-it-Q4_K_M.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-Q4_K_M.gguf) | Q4_K_M | 16.64GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
-| [gemma-2-27b-it-Q4_K_S.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-Q4_K_S.gguf) | Q4_K_S | 15.73GB | Slightly lower quality with more space savings, *recommended*. |
-| [gemma-2-27b-it-IQ4_XS.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-IQ4_XS.gguf) | IQ4_XS | 14.81GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
-| [gemma-2-27b-it-Q3_K_XL.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-Q3_K_XL.gguf) | Q3_K_XL | 15.91GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Lower quality but usable, good for low RAM availability. |
-| [gemma-2-27b-it-Q3_K_L.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-Q3_K_L.gguf) | Q3_K_L | 14.51GB | Lower quality but usable, good for low RAM availability. |
-| [gemma-2-27b-it-Q3_K_M.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-Q3_K_M.gguf) | Q3_K_M | 13.42GB | Even lower quality. |
-| [gemma-2-27b-it-IQ3_M.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-IQ3_M.gguf) | IQ3_M | 12.45GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
-| [gemma-2-27b-it-Q3_K_S.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-Q3_K_S.gguf) | Q3_K_S | 12.16GB | Low quality, not recommended. |
-| [gemma-2-27b-it-IQ3_XS.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-IQ3_XS.gguf) | IQ3_XS | 11.55GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
-| [gemma-2-27b-it-IQ3_XXS.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-IQ3_XXS.gguf) | IQ3_XXS | 10.75GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
-| [gemma-2-27b-it-Q2_K.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-Q2_K.gguf) | Q2_K | 10.44GB | Very low quality but surprisingly usable. |
-| [gemma-2-27b-it-IQ2_M.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-IQ2_M.gguf) | IQ2_M | 9.39GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
-| [gemma-2-27b-it-IQ2_S.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-IQ2_S.gguf) | IQ2_S | 8.65GB | Very low quality, uses SOTA techniques to be usable. |
-| [gemma-2-27b-it-IQ2_XS.gguf](https://huggingface.co/bartowski/gemma-2-27b-it-GGUF/blob/main/gemma-2-27b-it-IQ2_XS.gguf) | IQ2_XS | 8.39GB | Very low quality, uses SOTA techniques to be usable. |
-
-## Downloading using huggingface-cli
-
-First, make sure you have hugginface-cli installed:
-
-```
-pip install -U "huggingface_hub[cli]"
-```
+## Model Summary:
+Gemma 2 instruct is a a brand new model from Google in the Gemma family based on the technology from Gemini. Trained on a combination of web documents, code, and mathematics, this model should excel at anything you throw at it.<br>
+With 27B parameters, this fills in a really great gap between the typical ~8B and 70B models, and should be great for anyone with moderate VRAM availability.
 
-Then, you can target the specific file you want:
+## Prompt Template:
 
-```
-huggingface-cli download bartowski/gemma-2-27b-it-GGUF --include "gemma-2-27b-it-Q4_K_M.gguf" --local-dir ./
-```
+Choose the 'Google Gemma Instruct' preset in your LM Studio. 
 
-If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
+Under the hood, the model will see a prompt that's formatted like so:
 
 ```
-huggingface-cli download bartowski/gemma-2-27b-it-GGUF --include "gemma-2-27b-it-Q8_0.gguf/*" --local-dir gemma-2-27b-it-Q8_0
+<start_of_turn>user
+{prompt}<end_of_turn>
+<start_of_turn>model
 ```
 
-You can either specify a new local-dir (gemma-2-27b-it-Q8_0) or download them all in place (./)
-
-## Which file should I choose?
-
-A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
-
-The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
+Note that this model does not support a System prompt.
 
-If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
+## Technical Details
 
-If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
+Gemma 2 features the same extremely large vocabulary from release 1.1, which tends to help with multilingual and coding proficiency.
 
-Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
+Gemma 2 27B was trained on a wide dataset of 13 trillion tokens, more than twice as many as Gemma 1.1, and an extra 60% over the 9B model, using similar datasets including:
 
-If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
+- Web Documents: A diverse collection of web text ensures the model is exposed to a broad range of linguistic styles, topics, and vocabulary. Primarily English-language content.
+- Code: Exposing the model to code helps it to learn the syntax and patterns of programming languages, which improves its ability to generate code or understand code-related questions.
+- Mathematics: Training on mathematical text helps the model learn logical reasoning, symbolic representation, and to address mathematical queries.
 
-If you want to get more into the weeds, you can check out this extremely useful feature chart:
+For more details check out their blog post here: https://huggingface.co/blog/gemma2
 
-[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
+## Special thanks
 
-But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
+🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
 
-These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
+🙏 Special thanks to [Kalomaze](https://github.com/kalomaze) and [Dampf](https://github.com/Dampfinchen) for their work on the dataset (linked [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)) that was used for calculating the imatrix for all sizes.
 
-The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
+## Disclaimers
 
-Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
+LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model.  You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models.  LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.