Update README.md

README.md

@@ -10,75 +10,78 @@ tags:
 metrics:
 - code_eval
 quantized_by: bartowski
+lm_studio:
+  param_count: 6.7b
+  use_case: coding
+  release_date: 15-04-2024
+  model_creator: microsoft
+  prompt_template: alpaca
+  system_prompt: Below is an instruction that describes a task. Write a response that appropriately completes the request.
+  base_model: DeepseekCoder
+  original_repo: microsoft/wavecoder-ultra-6.7b
 ---
 
-## Llamacpp Quantizations of wavecoder-ultra-6.7b
+## 💫 Community Model> wavecoder-ultra-6.7b by Microsoft
 
-Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2675">b2675</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/microsoft/wavecoder-ultra-6.7b
+**Model creator:** [Microsoft](https://huggingface.co/microsoft)<br>
+**Original model**: [wavecoder-ultra-6.7b](https://huggingface.co/microsoft/wavecoder-ultra-6.7b)<br>
+**GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b2675](https://github.com/ggerganov/llama.cpp/releases/tag/b2675)<br>
 
-All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
+## Model Summary:
 
-## Prompt format
+WaveCoder ultra is a coding model created with 'Widepread And Versatile Enhanced' instruction tuning. It has exceptional generalization ability across different code-related tasks and has a high efficiency in generation.<br>
+This model should be used exclusively for coding, and will follow instructions for code generation.
 
-This seems to follow the DeepSeek coder format, aka Alpaca.
+## Prompt Template:
+
+Choose the `Alpaca` preset in your LM Studio.
+
+Under the hood, the model will see a prompt that's formatted like so:
 
 ```
-{system_prompt}
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
 ### Instruction: {prompt}
 ### Response:
 ```
 
-## Download a file (not the whole branch) from below:
+## Use case and examples
+
+WaveCoder ultra is fine tuned for code-related instruction following tasks, including code generation, summarization, repair, and translation.
+
+## Code Generation
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/X-MeWq5Aa0MTYUhEv2C4F.png)
+
+## Code Summarization
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/qeT5QL-ISLNaRr0NnNDzE.png)
 
-| Filename | Quant type | File Size | Description |
-| -------- | ---------- | --------- | ----------- |
-| [wavecoder-ultra-6.7b-Q8_0.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-Q8_0.gguf) | Q8_0 | 7.16GB | Extremely high quality, generally unneeded but max available quant. |
-| [wavecoder-ultra-6.7b-Q6_K.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-Q6_K.gguf) | Q6_K | 5.52GB | Very high quality, near perfect, *recommended*. |
-| [wavecoder-ultra-6.7b-Q5_K_M.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-Q5_K_M.gguf) | Q5_K_M | 4.78GB | High quality, *recommended*. |
-| [wavecoder-ultra-6.7b-Q5_K_S.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-Q5_K_S.gguf) | Q5_K_S | 4.65GB | High quality, *recommended*. |
-| [wavecoder-ultra-6.7b-Q4_K_M.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-Q4_K_M.gguf) | Q4_K_M | 4.08GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
-| [wavecoder-ultra-6.7b-Q4_K_S.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-Q4_K_S.gguf) | Q4_K_S | 3.85GB | Slightly lower quality with more space savings, *recommended*. |
-| [wavecoder-ultra-6.7b-IQ4_NL.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-IQ4_NL.gguf) | IQ4_NL | 3.82GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
-| [wavecoder-ultra-6.7b-IQ4_XS.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-IQ4_XS.gguf) | IQ4_XS | 3.62GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
-| [wavecoder-ultra-6.7b-Q3_K_L.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-Q3_K_L.gguf) | Q3_K_L | 3.59GB | Lower quality but usable, good for low RAM availability. |
-| [wavecoder-ultra-6.7b-Q3_K_M.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-Q3_K_M.gguf) | Q3_K_M | 3.29GB | Even lower quality. |
-| [wavecoder-ultra-6.7b-IQ3_M.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-IQ3_M.gguf) | IQ3_M | 3.11GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
-| [wavecoder-ultra-6.7b-IQ3_S.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-IQ3_S.gguf) | IQ3_S | 2.94GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
-| [wavecoder-ultra-6.7b-Q3_K_S.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-Q3_K_S.gguf) | Q3_K_S | 2.94GB | Low quality, not recommended. |
-| [wavecoder-ultra-6.7b-IQ3_XS.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-IQ3_XS.gguf) | IQ3_XS | 2.79GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
-| [wavecoder-ultra-6.7b-IQ3_XXS.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-IQ3_XXS.gguf) | IQ3_XXS | 2.58GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
-| [wavecoder-ultra-6.7b-Q2_K.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-Q2_K.gguf) | Q2_K | 2.53GB | Very low quality but surprisingly usable. |
-| [wavecoder-ultra-6.7b-IQ2_M.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-IQ2_M.gguf) | IQ2_M | 2.36GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
-| [wavecoder-ultra-6.7b-IQ2_S.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-IQ2_S.gguf) | IQ2_S | 2.19GB | Very low quality, uses SOTA techniques to be usable. |
-| [wavecoder-ultra-6.7b-IQ2_XS.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-IQ2_XS.gguf) | IQ2_XS | 2.03GB | Very low quality, uses SOTA techniques to be usable. |
-| [wavecoder-ultra-6.7b-IQ2_XXS.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-IQ2_XXS.gguf) | IQ2_XXS | 1.85GB | Lower quality, uses SOTA techniques to be usable. |
-| [wavecoder-ultra-6.7b-IQ1_M.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-IQ1_M.gguf) | IQ1_M | 1.65GB | Extremely low quality, *not* recommended. |
-| [wavecoder-ultra-6.7b-IQ1_S.gguf](https://huggingface.co/bartowski/wavecoder-ultra-6.7b-GGUF/blob/main/wavecoder-ultra-6.7b-IQ1_S.gguf) | IQ1_S | 1.52GB | Extremely low quality, *not* recommended. |
+## Code Repair
 
-## Which file should I choose?
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/NmtLNuIbB1vPmbunq1Q3p.png)
 
-A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
+## Code Translation
 
-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.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/FZroxLvNUVPlydyJlc0lU.png)
 
-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.
+The WaveCoder series of models is the result of a 'Widespread And Versatile Enchanced' (WAVE) instruction tuning with a highly refined dataset.
 
-Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
+Their 'CodeOcean' consists of 20,000 instruction instances across the 4 code-related tasks (generation, summarization, repair, translation) with instructions generated by GPT-3.5-turbo.
 
-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.
+To create this dataset, the team used existing raw code from GitHub CodeSearchNet, filtering for quality and diversity, then used a 'novel LLM-based Generator-Discriminator Framework' which involves generating supervised instruction data from the unsupervised open source code.
 
-If you want to get more into the weeds, you can check out this extremely useful feature chart:
+For further details and benchmarks, check out their arXiv paper [here](https://arxiv.org/abs/2312.14187)
 
-[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) for his dataset (linked [here](https://github.com/ggerganov/llama.cpp/discussions/5263)) that was used for calculating the imatrix for these quants, which improves the overall quality!
 
-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.