Update README.md

README.md

@@ -1,6 +1,5 @@
 ---
 license: gemma
-base_model: HuggingFaceH4/zephyr-7b-gemma-sft-v0.1
 tags:
 - alignment-handbook
 - generated_from_trainer
@@ -11,93 +10,60 @@ model-index:
   results: []
 quantized_by: bartowski
 pipeline_tag: text-generation
+lm_studio:
+  param_count: 8b
+  use_case: general
+  release_date: 13-06-2024
+  model_creator: SakanaAI
+  prompt_template: ChatML 
+  system_prompt: none
+  base_model: gemma
+  original_repo: SakanaAI/DiscoPOP-zephyr-7b-gemma
+base_model: SakanaAI/DiscoPOP-zephyr-7b-gemma
 ---
+## 💫 Community Model> DiscoPOP-zephyr-7b-gemma by Sakana AI
 
-## Llamacpp imatrix Quantizations of DiscoPOP-zephyr-7b-gemma
+*👾 [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)*.
 
-Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3145">b3145</a> (with a fix to conversion script) for quantization.
+**Model creator:** [Sakana AI](https://huggingface.co/SakanaAI)<br>
+**Original model**: [DiscoPOP-zephyr-7b-gemma](https://huggingface.co/SakanaAI/DiscoPOP-zephyr-7b-gemma)<br>
+**GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b3145](https://github.com/ggerganov/llama.cpp/releases/tag/b3145)<br>
 
-Original model: https://huggingface.co/SakanaAI/DiscoPOP-zephyr-7b-gemma
+## Model Summary:
 
-All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
+This model is based on the Zephyr 7b Gemma model, trained with a brand new technique called DiscoPOP. DiscoPOP is Sakana AI's Discovered Preference Optimization algorithm.<br>
+This training method is brand new, discovered through experimental model prompting to discover new bespoke training techniques.
 
-## Prompt format
+## Prompt template:
+
+Choose the `ChatML` preset in your LM Studio.
+
+Under the hood, the model will see a prompt that's formatted like so:
 
 ```
-<bos><|im_start|>system
-{system_prompt}<|im_end|>
 <|im_start|>user
 {prompt}<|im_end|>
 <|im_start|>assistant
 
 ```
 
-## Download a file (not the whole branch) from below:
-
-| Filename | Quant type | File Size | Description |
-| -------- | ---------- | --------- | ----------- |
-| [DiscoPOP-zephyr-7b-gemma-Q8_0.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-Q8_0.gguf) | Q8_0 | 9.81GB | Extremely high quality, generally unneeded but max available quant. |
-| [DiscoPOP-zephyr-7b-gemma-Q6_K.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-Q6_K.gguf) | Q6_K | 7.93GB | Very high quality, near perfect, *recommended*. |
-| [DiscoPOP-zephyr-7b-gemma-Q5_K_M.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-Q5_K_M.gguf) | Q5_K_M | 7.07GB | High quality, *recommended*. |
-| [DiscoPOP-zephyr-7b-gemma-Q5_K_S.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-Q5_K_S.gguf) | Q5_K_S | 6.90GB | High quality, *recommended*. |
-| [DiscoPOP-zephyr-7b-gemma-Q4_K_M.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-Q4_K_M.gguf) | Q4_K_M | 6.25GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
-| [DiscoPOP-zephyr-7b-gemma-Q4_K_S.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-Q4_K_S.gguf) | Q4_K_S | 5.97GB | Slightly lower quality with more space savings, *recommended*. |
-| [DiscoPOP-zephyr-7b-gemma-IQ4_XS.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-IQ4_XS.gguf) | IQ4_XS | 5.69GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
-| [DiscoPOP-zephyr-7b-gemma-Q3_K_L.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-Q3_K_L.gguf) | Q3_K_L | 5.63GB | Lower quality but usable, good for low RAM availability. |
-| [DiscoPOP-zephyr-7b-gemma-Q3_K_M.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-Q3_K_M.gguf) | Q3_K_M | 5.29GB | Even lower quality. |
-| [DiscoPOP-zephyr-7b-gemma-IQ3_M.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-IQ3_M.gguf) | IQ3_M | 5.03GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
-| [DiscoPOP-zephyr-7b-gemma-Q3_K_S.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-Q3_K_S.gguf) | Q3_K_S | 4.91GB | Low quality, not recommended. |
-| [DiscoPOP-zephyr-7b-gemma-IQ3_XS.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-IQ3_XS.gguf) | IQ3_XS | 4.72GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
-| [DiscoPOP-zephyr-7b-gemma-IQ3_XXS.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-IQ3_XXS.gguf) | IQ3_XXS | 4.51GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
-| [DiscoPOP-zephyr-7b-gemma-Q2_K.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-Q2_K.gguf) | Q2_K | 4.40GB | Very low quality but surprisingly usable. |
-| [DiscoPOP-zephyr-7b-gemma-IQ2_M.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-IQ2_M.gguf) | IQ2_M | 4.16GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
-| [DiscoPOP-zephyr-7b-gemma-IQ2_S.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-IQ2_S.gguf) | IQ2_S | 3.95GB | Very low quality, uses SOTA techniques to be usable. |
-| [DiscoPOP-zephyr-7b-gemma-IQ2_XS.gguf](https://huggingface.co/bartowski/DiscoPOP-zephyr-7b-gemma-GGUF/blob/main/DiscoPOP-zephyr-7b-gemma-IQ2_XS.gguf) | IQ2_XS | 3.84GB | 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]"
-```
-
-Then, you can target the specific file you want:
-
-```
-huggingface-cli download bartowski/DiscoPOP-zephyr-7b-gemma-GGUF --include "DiscoPOP-zephyr-7b-gemma-Q4_K_M.gguf" --local-dir ./
-```
-
-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:
-
-```
-huggingface-cli download bartowski/DiscoPOP-zephyr-7b-gemma-GGUF --include "DiscoPOP-zephyr-7b-gemma-Q8_0.gguf/*" --local-dir DiscoPOP-zephyr-7b-gemma-Q8_0
-```
-
-You can either specify a new local-dir (DiscoPOP-zephyr-7b-gemma-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.
-
-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.
+This model is not tuned for a system prompt.
 
-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.
+## Technical Details
 
-Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
+DiscoPOP was discovered through a new method proposed by Sakana AI. In this method, an LLM is prompted to propose and implement new preference optimization loss functions based on previously-evaluated performance metrics.<br>
+This process leads to the discovery of previously-unknown preference optimization algorithms. DiscoPOP represents the best performing discovered preference optimizer.<br>
 
-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.
+DiscoPOP achieves a higher score (Reward) while deviating less from the base model (KL Divergence), compared to existing state-of-the-art methods such as DPO.
 
-If you want to get more into the weeds, you can check out this extremely useful feature chart:
+For a deeper analysis and additional details, you can read their blog post here: https://sakana.ai/llm-squared/
 
-[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/) 
 
-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.