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README.md

@@ -1,114 +1,41 @@
+
 ---
-base_model: mistralai/Mistral-Large-Instruct-2407
-language:
-- en
-- fr
-- de
-- es
-- it
-- pt
-- zh
-- ja
-- ru
-- ko
-license: other
-license_name: mrl
-license_link: https://mistral.ai/licenses/MRL-0.1.md
-pipeline_tag: text-generation
 quantized_by: bartowski
-extra_gated_description: If you want to learn more about how we process your personal
-  data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
+pipeline_tag: text-generation
 ---
+## 💫 Community Model> Mistral Large Instruct 2407 by Mistralai
 
-## Llamacpp imatrix Quantizations of Mistral-Large-Instruct-2407
-
-Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3441">b3441</a> for quantization.
-
-Original model: https://huggingface.co/mistralai/Mistral-Large-Instruct-2407
-
-All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
-
-Run them in [LM Studio](https://lmstudio.ai/)
-
-## Prompt format
-
-No chat template specified so default is used. This may be incorrect, check original model card for details.
-
-```
-<s>[INST] <<SYS>>
-{system_prompt}
-<</SYS>>
-
-{prompt} [/INST]  </s>
-```
-
-## Download a file (not the whole branch) from below:
-
-| Filename | Quant type | File Size | Split | Description |
-| -------- | ---------- | --------- | ----- | ----------- |
-| [Mistral-Large-Instruct-2407-Q4_K_M.gguf](https://huggingface.co/bartowski/Mistral-Large-Instruct-2407-GGUF/tree/main/Mistral-Large-Instruct-2407-Q4_K_M) | Q4_K_M | 73.22GB | true | Good quality, default size for must use cases, *recommended*. |
-| [Mistral-Large-Instruct-2407-IQ4_XS.gguf](https://huggingface.co/bartowski/Mistral-Large-Instruct-2407-GGUF/tree/main/Mistral-Large-Instruct-2407-IQ4_XS) | IQ4_XS | 65.43GB | true | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
-| [Mistral-Large-Instruct-2407-Q3_K_M.gguf](https://huggingface.co/bartowski/Mistral-Large-Instruct-2407-GGUF/tree/main/Mistral-Large-Instruct-2407-Q3_K_M) | Q3_K_M | 59.10GB | true | Low quality. |
-| [Mistral-Large-Instruct-2407-IQ3_M.gguf](https://huggingface.co/bartowski/Mistral-Large-Instruct-2407-GGUF/tree/main/Mistral-Large-Instruct-2407-IQ3_M) | IQ3_M | 55.28GB | true | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
-| [Mistral-Large-Instruct-2407-Q3_K_S.gguf](https://huggingface.co/bartowski/Mistral-Large-Instruct-2407-GGUF/tree/main/Mistral-Large-Instruct-2407-Q3_K_S) | Q3_K_S | 52.85GB | true | Low quality, not recommended. |
-| [Mistral-Large-Instruct-2407-IQ3_XXS.gguf](https://huggingface.co/bartowski/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-IQ3_XXS.gguf) | IQ3_XXS | 47.01GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
-| [Mistral-Large-Instruct-2407-Q2_K.gguf](https://huggingface.co/bartowski/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-Q2_K.gguf) | Q2_K | 45.20GB | false | Very low quality but surprisingly usable. |
-| [Mistral-Large-Instruct-2407-IQ2_M.gguf](https://huggingface.co/bartowski/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-IQ2_M.gguf) | IQ2_M | 41.62GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
-| [Mistral-Large-Instruct-2407-IQ2_XS.gguf](https://huggingface.co/bartowski/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-IQ2_XS.gguf) | IQ2_XS | 36.08GB | false | Low quality, uses SOTA techniques to be usable. |
-| [Mistral-Large-Instruct-2407-IQ2_XXS.gguf](https://huggingface.co/bartowski/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-IQ2_XXS.gguf) | IQ2_XXS | 32.43GB | false | Very low quality, uses SOTA techniques to be usable. |
-| [Mistral-Large-Instruct-2407-IQ1_M.gguf](https://huggingface.co/bartowski/Mistral-Large-Instruct-2407-GGUF/blob/main/Mistral-Large-Instruct-2407-IQ1_M.gguf) | IQ1_M | 28.39GB | false | Extremely low quality, *not* recommended. |
-
-## Credits
+*👾 [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)*.
 
-Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset
+**Model creator:** [mistralai](https://huggingface.co/mistralai)<br>
+**Original model**: [Mistral-Large-Instruct-2407](https://huggingface.co/mistralai/Mistral-Large-Instruct-2407)<br>
+**GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b3441](https://github.com/ggerganov/llama.cpp/releases/tag/b3441)<br>
 
-Thank you ZeroWw for the inspiration to experiment with embed/output
+## Model Summary:
+Mistral Large 2 has a 128k context window and supports dozens of languages including French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, and Korean, along with 80+ coding languages including Python, Java, C, C++, JavaScript, and Bash.
 
-## Downloading using huggingface-cli
+## Prompt Template:
 
-First, make sure you have hugginface-cli installed:
+Choose the `Mistral Instruct` preset in your LM Studio.
+Under the hood, the model will see a prompt that's formatted like so:
 
 ```
-pip install -U "huggingface_hub[cli]"
+<s>[INST] {prompt}[/INST] </s>
 ```
 
-Then, you can target the specific file you want:
-
-```
-huggingface-cli download bartowski/Mistral-Large-Instruct-2407-GGUF --include "Mistral-Large-Instruct-2407-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/Mistral-Large-Instruct-2407-GGUF --include "Mistral-Large-Instruct-2407-Q8_0.gguf/*" --local-dir Mistral-Large-Instruct-2407-Q8_0
-```
-
-You can either specify a new local-dir (Mistral-Large-Instruct-2407-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.
-
-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.
-
-Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
-
-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.
+## Technical Details
+Mistral Large 2 features enhanced instruction-following and conversational capabilities. Additionally, a significant effort was also devoted to enhancing the model’s reasoning capabilities and decreasing the model’s tendency to “hallucinate” or generate plausible-sounding but factually incorrect or irrelevant information. This was achieved by fine-tuning the model to be more cautious and discerning in its responses, ensuring that it provides reliable and accurate outputs.
 
-If you want to get more into the weeds, you can check out this extremely useful feature chart:
+Mistral Large 2 is equipped with enhanced function calling and retrieval skills and has undergone training to proficiently execute both parallel and sequential function calls, enabling it to serve as the power engine of complex business applications.
 
-[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
+Mistral Large 2 was trained on a large proportion of multilingual data. In particular, it excels in English, French, German, Spanish, Italian, Portuguese, Dutch, Russian, Chinese, Japanese, Korean, Arabic, and Hindi. Below are the performance results of Mistral Large 2 on the multilingual MMLU benchmark, compared to the previous Mistral Large, Llama 3.1 models, and to Cohere’s Command R+.
 
-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
 
-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 [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.
 
-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.
+🙏 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 the IQ1_M and IQ2_XS quants, which makes them usable even at their tiny size!
 
-Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
+## Disclaimers
 
+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.