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---
base_model: Ministral-8B-Instruct-2410-HF
language:
- en
- fr
- de
- es
- it
- pt
- zh
- ja
- ru
- ko
library_name: vllm
license: other
license_name: mrl
license_link: https://mistral.ai/licenses/MRL-0.1.md
tags:
- mlx
inference: false
extra_gated_prompt: '# Mistral AI Research License
If You want to use a Mistral Model, a Derivative or an Output for any purpose that
is not expressly authorized under this Agreement, You must request a license from
Mistral AI, which Mistral AI may grant to You in Mistral AI''s sole discretion.
To discuss such a license, please contact Mistral AI via the website contact form:
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## 1. Scope and acceptance
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or Distribution of any Mistral Model by You, regardless of the source You obtained
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## 2. License
**2.1. Grant of rights**. Subject to Section 3 below, Mistral AI hereby grants
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Subject to Section 3 below, You may Distribute copies of the Mistral Model and/or
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Model; and Any terms and conditions You impose on any third-party recipients relating
to Derivatives made by or for You shall neither limit such third-party recipients''
use of the Mistral Model or any Derivatives made by or for Mistral AI in accordance
with the Mistral AI Research License nor conflict with any of its terms and conditions.
## 3. Limitations
**3.1. Misrepresentation.** You must not misrepresent or imply, through any means,
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## 5. Liability
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## 7. Termination
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## 8. General provisions
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the International Sale of Goods does not apply to this Agreement.
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## 9. Definitions
"Agreement": means this Mistral AI Research License agreement governing the access,
use, and Distribution of the Mistral Models, Derivatives and Outputs.
"Derivative": means any (i) modified version of the Mistral Model (including but
not limited to any customized or fine-tuned version thereof), (ii) work based on
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"Mistral AI", "We" or "Us": means Mistral AI, a French société par actions simplifiée
registered in the Paris commercial registry under the number 952 418 325, and having
its registered seat at 15, rue des Halles, 75001 Paris.
"Mistral Model": means the foundational large language model(s), and its elements
which include algorithms, software, instructed checkpoints, parameters, source code
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if any, the technical documentation, manuals and instructions for the use and operation
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is solely for (a) personal, scientific or academic research, and (b) for non-profit
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"Outputs": means any content generated by the operation of the Mistral Models or
the Derivatives from a prompt (i.e., text instructions) provided by users. For
the avoidance of doubt, Outputs do not include any components of a Mistral Models,
such as any fine-tuned versions of the Mistral Models, the weights, or parameters.
"You": means the individual or entity entering into this Agreement with Mistral
AI.
*Mistral AI processes your personal data below to provide the model and enforce
its license. If you are affiliated with a commercial entity, we may also send you
communications about our models. For more information on your rights and data handling,
please see our <a href="https://mistral.ai/terms/">privacy policy</a>.*'
extra_gated_fields:
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Country: country
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extra_gated_description: Mistral AI processes your personal data below to provide
the model and enforce its license. If you are affiliated with a commercial entity,
we may also send you communications about our models. For more information on your
rights and data handling, please see our <a href="https://mistral.ai/terms/">privacy
policy</a>.
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---
# mlx-community/Ministral-8B-Instruct-2410-4bit
The Model [mlx-community/Ministral-8B-Instruct-2410-4bit](https://huggingface.co./mlx-community/Ministral-8B-Instruct-2410-4bit) was converted to MLX format from [mistralai/Ministral-8B-Instruct-2410](https://huggingface.co./mistralai/Ministral-8B-Instruct-2410) using mlx-lm version **0.19.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Ministral-8B-Instruct-2410-4bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```