Fix readme

README.md

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+# Model Card for Mistral-Large-Instruct-2411-MLX
 
----
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-
-# Model Card for  Mistral-Large-Instruct-2411-Q2-MLX
-
-<!-- Provide a quick summary of what the model is/does. [Optional] -->
 This is a 2bit quantization of the Mistral Large Instruct 2411 model for MLX (Apple silicon). It was created using the mlx-lm library with the following CLI command:
 mlx_lm.convert \
     --hf-path /path/to/your/fp16/model \
@@ -17,250 +7,57 @@ mlx_lm.convert \
     --q-bits 2 \
     --q-group-size 32
 
-Original model is here:
-https://huggingface.co/mistralai/Mistral-Large-Instruct-2411
+## Quantized Versions
 
-Mistral model usage is governed by the Mistral Research License, viewable at the original model page and, at the time of upload, directly at this link:
-https://mistral.ai/licenses/MRL-0.1.md
+- [2-bit Quantization (Q2)](https://huggingface.co/zachlandes/Mistral-Large-Instruct-2411-Q2-MLX)
+- [4-bit Quantization (Q4)](https://huggingface.co/zachlandes/Mistral-Large-Instruct-2411-Q4-MLX)
 
+Each version is optimized for specific memory and performance trade-offs. 
 
+## Original Model
 
+The original Mistral-Large-Instruct-2411 model is available [here](https://huggingface.co/mistralai/Mistral-Large-Instruct-2411). Mistral model usage is governed by the [Mistral Research License](https://mistral.ai/licenses/MRL-0.1.md).
 
-#  Table of Contents
+## License
+
+This model family is governed by the [Mistral Research License](https://mistral.ai/licenses/MRL-0.1.md). Please review the license terms before use.
+
+## Table of Contents
 
-- [Model Card for  Mistral-Large-Instruct-2411-Q2-MLX](#model-card-for--model_id-)
-- [Table of Contents](#table-of-contents)
-- [Table of Contents](#table-of-contents-1)
 - [Model Details](#model-details)
   - [Model Description](#model-description)
 - [Uses](#uses)
   - [Direct Use](#direct-use)
-  - [Downstream Use [Optional]](#downstream-use-optional)
   - [Out-of-Scope Use](#out-of-scope-use)
 - [Bias, Risks, and Limitations](#bias-risks-and-limitations)
   - [Recommendations](#recommendations)
-- [Training Details](#training-details)
-  - [Training Data](#training-data)
-  - [Training Procedure](#training-procedure)
-    - [Preprocessing](#preprocessing)
-    - [Speeds, Sizes, Times](#speeds-sizes-times)
-- [Evaluation](#evaluation)
-  - [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
-    - [Testing Data](#testing-data)
-    - [Factors](#factors)
-    - [Metrics](#metrics)
-  - [Results](#results)
-- [Model Examination](#model-examination)
-- [Environmental Impact](#environmental-impact)
-- [Technical Specifications [optional]](#technical-specifications-optional)
-  - [Model Architecture and Objective](#model-architecture-and-objective)
-  - [Compute Infrastructure](#compute-infrastructure)
-    - [Hardware](#hardware)
-    - [Software](#software)
-- [Citation](#citation)
-- [Glossary [optional]](#glossary-optional)
-- [More Information [optional]](#more-information-optional)
-- [Model Card Authors [optional]](#model-card-authors-optional)
-- [Model Card Contact](#model-card-contact)
-- [How to Get Started with the Model](#how-to-get-started-with-the-model)
-
-
-# Model Details
-
-## Model Description
-
-<!-- Provide a longer summary of what this model is/does. -->
-This is a 2bit quantization of the Mistral Large Instruct 2411 model for MLX (Apple silicon). It was created using the mlx-lm library with the following CLI command:
-mlx_lm.convert \
-    --hf-path /path/to/your/fp16/model \
-    -q \
-    --q-bits 2 \
-    --q-group-size 32
-
-Original model is here:
-https://huggingface.co/mistralai/Mistral-Large-Instruct-2411
-
-Mistral model usage is governed by the Mistral Research License, viewable at the original model page and, at the time of upload, directly at this link:
-https://mistral.ai/licenses/MRL-0.1.md
-
-- **Developed by:** More information needed
-- **Shared by [Optional]:** More information needed
-- **Model type:** Language model
-- **Language(s) (NLP):** en, fra, deu, spa, ita, por, zho, jpn, rus, kor
-- **License:** apache-2.0
-- **Parent Model:** More information needed
-- **Resources for more information:** More information needed
-
-
-
-# Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-## Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
-
-
-
-
-## Downstream Use [Optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
- 
-
-
-
-## Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
-
-
-
-
-# Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
-
-
-## Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-
-
-
-
-# Training Details
-
-## Training Data
-
-<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-More information on training data needed
-
-
-## Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-### Preprocessing
-
-More information needed
-
-### Speeds, Sizes, Times
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-More information needed
- 
-# Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-## Testing Data, Factors & Metrics
-
-### Testing Data
-
-<!-- This should link to a Data Card if possible. -->
-
-More information needed
-
-
-### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-More information needed
-
-### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-More information needed
-
-## Results 
-
-More information needed
-
-# Model Examination
-
-More information needed
-
-# Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** More information needed
-- **Hours used:** More information needed
-- **Cloud Provider:** More information needed
-- **Compute Region:** More information needed
-- **Carbon Emitted:** More information needed
-
-# Technical Specifications [optional]
-
-## Model Architecture and Objective
-
-More information needed
-
-## Compute Infrastructure
-
-More information needed
-
-### Hardware
-
-More information needed
-
-### Software
-
-More information needed
-
-# Citation
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-More information needed
-
-**APA:**
-
-More information needed
-
-# Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-More information needed
-
-# More Information [optional]
+- [Technical Specifications](#technical-specifications)
+- [How to Get Started](#how-to-get-started)
 
-More information needed
+## Model Details
 
-# Model Card Authors [optional]
+### Model Description
 
-<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
+The Mistral-Large-Instruct-2411-MLX family includes quantized versions of the Mistral Large Instruct 2411 model, optimized for deployment on MLX (Apple Silicon). The quantization reduces memory usage and inference latency, enabling efficient deployment on resource-constrained systems.
 
-More information needed
+- **Developed by:** Mistral AI
+- **Model type:** Large language model
+- **Language(s):** English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese, Russian, Korean
+- **Quantization levels:** 2-bit (Q2), 4-bit (Q4)
 
-# Model Card Contact
+## Technical Specifications
 
-More information needed
+- **Parent Model:** [Mistral-Large-Instruct-2411](https://huggingface.co/mistralai/Mistral-Large-Instruct-2411)
+- **Quantization:** 2-bit (Q2), 4-bit (Q4)
+- **Framework:** MLX (`mlx-lm` library)
 
-# How to Get Started with the Model
+## How to Get Started
 
-Use the code below to get started with the model.
+Visit the individual quantized repositories for details and usage instructions:
 
-<details>
-<summary> Click to expand </summary>
+- [2-bit Quantization (Q2)](https://huggingface.co/zachlandes/Mistral-Large-Instruct-2411-Q2-MLX)
+- [4-bit Quantization (Q4)](https://huggingface.co/zachlandes/Mistral-Large-Instruct-2411-Q4-MLX)
 
-More information needed
+## Model Card Contact
 
-</details>
+For inquiries, contact [Zach Landes](https://www.linkedin.com/in/zachlandes/).