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---
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license: Apache License 2.0
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base_model: mistralai/Ministral-8B-Instruct-2410
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language:
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- en
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- fr
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- de
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- es
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- it
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- pt
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- zh
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- ja
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- ru
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- ko
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license: other
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license_name: mrl
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license_link: https://mistral.ai/licenses/MRL-0.1.md
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pipeline_tag: text-generation
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quantized_by: bartowski
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inference: false
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extra_gated_prompt: '# Mistral AI Research License
|
||||
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||||
#model-type:
|
||||
##如 gpt、phi、llama、chatglm、baichuan 等
|
||||
#- gpt
|
||||
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:
|
||||
https://mistral.ai/contact/
|
||||
|
||||
#domain:
|
||||
##如 nlp、cv、audio、multi-modal
|
||||
#- nlp
|
||||
## 1. Scope and acceptance
|
||||
|
||||
#language:
|
||||
##语言代码列表 https://help.aliyun.com/document_detail/215387.html?spm=a2c4g.11186623.0.0.9f8d7467kni6Aa
|
||||
#- cn
|
||||
**1.1. Scope of the Agreement.** This Agreement applies to any use, modification,
|
||||
or Distribution of any Mistral Model by You, regardless of the source You obtained
|
||||
a copy of such Mistral Model.
|
||||
|
||||
#metrics:
|
||||
##如 CIDEr、Blue、ROUGE 等
|
||||
#- CIDEr
|
||||
**1.2. Acceptance.** By accessing, using, modifying, Distributing a Mistral Model,
|
||||
or by creating, using or distributing a Derivative of the Mistral Model, You agree
|
||||
to be bound by this Agreement.
|
||||
|
||||
#tags:
|
||||
##各种自定义，包括 pretrained、fine-tuned、instruction-tuned、RL-tuned 等训练方法和其他
|
||||
#- pretrained
|
||||
**1.3. Acceptance on behalf of a third-party.** If You accept this Agreement on
|
||||
behalf of Your employer or another person or entity, You warrant and represent that
|
||||
You have the authority to act and accept this Agreement on their behalf. In such
|
||||
a case, the word "You" in this Agreement will refer to Your employer or such other
|
||||
person or entity.
|
||||
|
||||
#tools:
|
||||
##如 vllm、fastchat、llamacpp、AdaSeq 等
|
||||
#- vllm
|
||||
## 2. License
|
||||
|
||||
**2.1. Grant of rights**. Subject to Section 3 below, Mistral AI hereby grants
|
||||
You a non-exclusive, royalty-free, worldwide, non-sublicensable, non-transferable,
|
||||
limited license to use, copy, modify, and Distribute under the conditions provided
|
||||
in Section 2.2 below, the Mistral Model and any Derivatives made by or for Mistral
|
||||
AI and to create Derivatives of the Mistral Model.
|
||||
|
||||
**2.2. Distribution of Mistral Model and Derivatives made by or for Mistral AI.**
|
||||
Subject to Section 3 below, You may Distribute copies of the Mistral Model and/or
|
||||
Derivatives made by or for Mistral AI, under the following conditions: You must
|
||||
make available a copy of this Agreement to third-party recipients of the Mistral
|
||||
Models and/or Derivatives made by or for Mistral AI you Distribute, it being specified
|
||||
that any rights to use the Mistral Models and/or Derivatives made by or for Mistral
|
||||
AI shall be directly granted by Mistral AI to said third-party recipients pursuant
|
||||
to the Mistral AI Research License agreement executed between these parties; You
|
||||
must retain in all copies of the Mistral Models the following attribution notice
|
||||
within a "Notice" text file distributed as part of such copies: "Licensed by Mistral
|
||||
AI under the Mistral AI Research License".
|
||||
|
||||
**2.3. Distribution of Derivatives made by or for You.** Subject to Section 3 below,
|
||||
You may Distribute any Derivatives made by or for You under additional or different
|
||||
terms and conditions, provided that: In any event, the use and modification of Mistral
|
||||
Model and/or Derivatives made by or for Mistral AI shall remain governed by the
|
||||
terms and conditions of this Agreement; You include in any such Derivatives made
|
||||
by or for You prominent notices stating that You modified the concerned Mistral
|
||||
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,
|
||||
that the Derivatives made by or for You and/or any modified version of the Mistral
|
||||
Model You Distribute under your name and responsibility is an official product of
|
||||
Mistral AI or has been endorsed, approved or validated by Mistral AI, unless You
|
||||
are authorized by Us to do so in writing.
|
||||
|
||||
**3.2. Usage Limitation.** You shall only use the Mistral Models, Derivatives (whether
|
||||
or not created by Mistral AI) and Outputs for Research Purposes.
|
||||
|
||||
## 4. Intellectual Property
|
||||
|
||||
**4.1. Trademarks.** No trademark licenses are granted under this Agreement, and
|
||||
in connection with the Mistral Models, You may not use any name or mark owned by
|
||||
or associated with Mistral AI or any of its affiliates, except (i) as required for
|
||||
reasonable and customary use in describing and Distributing the Mistral Models and
|
||||
Derivatives made by or for Mistral AI and (ii) for attribution purposes as required
|
||||
by this Agreement.
|
||||
|
||||
**4.2. Outputs.** We claim no ownership rights in and to the Outputs. You are solely
|
||||
responsible for the Outputs You generate and their subsequent uses in accordance
|
||||
with this Agreement. Any Outputs shall be subject to the restrictions set out in
|
||||
Section 3 of this Agreement.
|
||||
|
||||
**4.3. Derivatives.** By entering into this Agreement, You accept that any Derivatives
|
||||
that You may create or that may be created for You shall be subject to the restrictions
|
||||
set out in Section 3 of this Agreement.
|
||||
|
||||
## 5. Liability
|
||||
|
||||
**5.1. Limitation of liability.** In no event, unless required by applicable law
|
||||
(such as deliberate and grossly negligent acts) or agreed to in writing, shall Mistral
|
||||
AI be liable to You for damages, including any direct, indirect, special, incidental,
|
||||
or consequential damages of any character arising as a result of this Agreement
|
||||
or out of the use or inability to use the Mistral Models and Derivatives (including
|
||||
but not limited to damages for loss of data, loss of goodwill, loss of expected
|
||||
profit or savings, work stoppage, computer failure or malfunction, or any damage
|
||||
caused by malware or security breaches), even if Mistral AI has been advised of
|
||||
the possibility of such damages.
|
||||
|
||||
**5.2. Indemnification.** You agree to indemnify and hold harmless Mistral AI from
|
||||
and against any claims, damages, or losses arising out of or related to Your use
|
||||
or Distribution of the Mistral Models and Derivatives.
|
||||
|
||||
## 6. Warranty
|
||||
|
||||
**6.1. Disclaimer.** Unless required by applicable law or prior agreed to by Mistral
|
||||
AI in writing, Mistral AI provides the Mistral Models and Derivatives on an "AS
|
||||
IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied,
|
||||
including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT,
|
||||
MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. Mistral AI does not represent
|
||||
nor warrant that the Mistral Models and Derivatives will be error-free, meet Your
|
||||
or any third party''s requirements, be secure or will allow You or any third party
|
||||
to achieve any kind of result or generate any kind of content. You are solely responsible
|
||||
for determining the appropriateness of using or Distributing the Mistral Models
|
||||
and Derivatives and assume any risks associated with Your exercise of rights under
|
||||
this Agreement.
|
||||
|
||||
## 7. Termination
|
||||
|
||||
**7.1. Term.** This Agreement is effective as of the date of your acceptance of
|
||||
this Agreement or access to the concerned Mistral Models or Derivatives and will
|
||||
continue until terminated in accordance with the following terms.
|
||||
|
||||
**7.2. Termination.** Mistral AI may terminate this Agreement at any time if You
|
||||
are in breach of this Agreement. Upon termination of this Agreement, You must cease
|
||||
to use all Mistral Models and Derivatives and shall permanently delete any copy
|
||||
thereof. The following provisions, in their relevant parts, will survive any termination
|
||||
or expiration of this Agreement, each for the duration necessary to achieve its
|
||||
own intended purpose (e.g. the liability provision will survive until the end of
|
||||
the applicable limitation period):Sections 5 (Liability), 6(Warranty), 7 (Termination)
|
||||
and 8 (General Provisions).
|
||||
|
||||
**7.3. Litigation.** If You initiate any legal action or proceedings against Us
|
||||
or any other entity (including a cross-claim or counterclaim in a lawsuit), alleging
|
||||
that the Model or a Derivative, or any part thereof, infringe upon intellectual
|
||||
property or other rights owned or licensable by You, then any licenses granted to
|
||||
You under this Agreement will immediately terminate as of the date such legal action
|
||||
or claim is filed or initiated.
|
||||
|
||||
## 8. General provisions
|
||||
|
||||
**8.1. Governing laws.** This Agreement will be governed by the laws of France,
|
||||
without regard to choice of law principles, and the UN Convention on Contracts for
|
||||
the International Sale of Goods does not apply to this Agreement.
|
||||
|
||||
**8.2. Competent jurisdiction.** The courts of Paris shall have exclusive jurisdiction
|
||||
of any dispute arising out of this Agreement.
|
||||
|
||||
**8.3. Severability.** If any provision of this Agreement is held to be invalid,
|
||||
illegal or unenforceable, the remaining provisions shall be unaffected thereby and
|
||||
remain valid as if such provision had not been set forth herein.
|
||||
|
||||
## 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
|
||||
the Mistral Model, or (iii) any other derivative work thereof.
|
||||
|
||||
"Distribution", "Distributing", "Distribute" or "Distributed": means supplying,
|
||||
providing or making available, by any means, a copy of the Mistral Models and/or
|
||||
the Derivatives as the case may be, subject to Section 3 of this Agreement.
|
||||
|
||||
"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
|
||||
(inference code, evaluation code and, if applicable, fine-tuning code) and any other
|
||||
elements associated thereto made available by Mistral AI under this Agreement, including,
|
||||
if any, the technical documentation, manuals and instructions for the use and operation
|
||||
thereof.
|
||||
|
||||
"Research Purposes": means any use of a Mistral Model, Derivative, or Output that
|
||||
is solely for (a) personal, scientific or academic research, and (b) for non-profit
|
||||
and non-commercial purposes, and not directly or indirectly connected to any commercial
|
||||
activities or business operations. For illustration purposes, Research Purposes
|
||||
does not include (1) any usage of the Mistral Model, Derivative or Output by individuals
|
||||
or contractors employed in or engaged by companies in the context of (a) their daily
|
||||
tasks, or (b) any activity (including but not limited to any testing or proof-of-concept)
|
||||
that is intended to generate revenue, nor (2) any Distribution by a commercial entity
|
||||
of the Mistral Model, Derivative or Output whether in return for payment or free
|
||||
of charge, in any medium or form, including but not limited to through a hosted
|
||||
or managed service (e.g. SaaS, cloud instances, etc.), or behind a software layer.
|
||||
|
||||
"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:
|
||||
First Name: text
|
||||
Last Name: text
|
||||
Country: country
|
||||
Affiliation: text
|
||||
Job title: text
|
||||
I understand that I can only use the model, any derivative versions and their outputs for non-commercial research purposes: checkbox
|
||||
? I understand that if I am a commercial entity, I am not permitted to use or distribute
|
||||
the model internally or externally, or expose it in my own offerings without a
|
||||
commercial license
|
||||
: checkbox
|
||||
? I understand that if I upload the model, or any derivative version, on any platform,
|
||||
I must include the Mistral Research License
|
||||
: checkbox
|
||||
? I understand that for commercial use of the model, I can contact Mistral or use
|
||||
the Mistral AI API on la Plateforme or any of our cloud provider partners
|
||||
: checkbox
|
||||
? By clicking Submit below I accept the terms of the license and acknowledge that
|
||||
the information I provide will be collected stored processed and shared in accordance
|
||||
with the Mistral Privacy Policy
|
||||
: checkbox
|
||||
geo: ip_location
|
||||
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>.
|
||||
extra_gated_button_content: Submit
|
||||
---
|
||||
### 当前模型的贡献者未提供更加详细的模型介绍。模型文件和权重，可浏览“模型文件”页面获取。
|
||||
#### 您可以通过如下git clone命令，或者ModelScope SDK来下载模型
|
||||
|
||||
SDK下载
|
||||
```bash
|
||||
#安装ModelScope
|
||||
pip install modelscope
|
||||
## Llamacpp imatrix Quantizations of Ministral-8B-Instruct-2410
|
||||
|
||||
# This is based on the officially merged safetensors for Ministral, however there may still be changes required to llama.cpp for full performance
|
||||
|
||||
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3930">b3930</a> for quantization.
|
||||
|
||||
Original model: https://huggingface.co/mistralai/Ministral-8B-Instruct-2410
|
||||
|
||||
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
|
||||
|
||||
```
|
||||
```python
|
||||
#SDK模型下载
|
||||
from modelscope import snapshot_download
|
||||
model_dir = snapshot_download('bartowski/Ministral-8B-Instruct-2410-GGUF')
|
||||
```
|
||||
Git下载
|
||||
```
|
||||
#Git模型下载
|
||||
git clone https://www.modelscope.cn/bartowski/Ministral-8B-Instruct-2410-GGUF.git
|
||||
<s>[INST]{prompt}[/INST]
|
||||
```
|
||||
|
||||
<p style="color: lightgrey;">如果您是本模型的贡献者，我们邀请您根据<a href="https://modelscope.cn/docs/ModelScope%E6%A8%A1%E5%9E%8B%E6%8E%A5%E5%85%A5%E6%B5%81%E7%A8%8B%E6%A6%82%E8%A7%88" style="color: lightgrey; text-decoration: underline;">模型贡献文档</a>，及时完善模型卡片内容。</p>
|
||||
## What's new:
|
||||
|
||||
Update to official repo
|
||||
|
||||
## Download a file (not the whole branch) from below:
|
||||
|
||||
| Filename | Quant type | File Size | Split | Description |
|
||||
| -------- | ---------- | --------- | ----- | ----------- |
|
||||
| [Ministral-8B-Instruct-2410-f16.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-f16.gguf) | f16 | 16.05GB | false | Full F16 weights. |
|
||||
| [Ministral-8B-Instruct-2410-Q8_0.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q8_0.gguf) | Q8_0 | 8.53GB | false | Extremely high quality, generally unneeded but max available quant. |
|
||||
| [Ministral-8B-Instruct-2410-Q6_K_L.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q6_K_L.gguf) | Q6_K_L | 6.85GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
|
||||
| [Ministral-8B-Instruct-2410-Q6_K.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q6_K.gguf) | Q6_K | 6.59GB | false | Very high quality, near perfect, *recommended*. |
|
||||
| [Ministral-8B-Instruct-2410-Q5_K_L.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q5_K_L.gguf) | Q5_K_L | 6.06GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
|
||||
| [Ministral-8B-Instruct-2410-Q5_K_M.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q5_K_M.gguf) | Q5_K_M | 5.72GB | false | High quality, *recommended*. |
|
||||
| [Ministral-8B-Instruct-2410-Q5_K_S.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q5_K_S.gguf) | Q5_K_S | 5.59GB | false | High quality, *recommended*. |
|
||||
| [Ministral-8B-Instruct-2410-Q4_K_L.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q4_K_L.gguf) | Q4_K_L | 5.31GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
|
||||
| [Ministral-8B-Instruct-2410-Q4_K_M.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q4_K_M.gguf) | Q4_K_M | 4.91GB | false | Good quality, default size for must use cases, *recommended*. |
|
||||
| [Ministral-8B-Instruct-2410-Q3_K_XL.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q3_K_XL.gguf) | Q3_K_XL | 4.80GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
|
||||
| [Ministral-8B-Instruct-2410-Q4_K_S.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q4_K_S.gguf) | Q4_K_S | 4.69GB | false | Slightly lower quality with more space savings, *recommended*. |
|
||||
| [Ministral-8B-Instruct-2410-Q4_0.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q4_0.gguf) | Q4_0 | 4.67GB | false | Legacy format, generally not worth using over similarly sized formats |
|
||||
| [Ministral-8B-Instruct-2410-Q4_0_8_8.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q4_0_8_8.gguf) | Q4_0_8_8 | 4.66GB | false | Optimized for ARM inference. Requires 'sve' support (see link below). *Don't use on Mac or Windows*. |
|
||||
| [Ministral-8B-Instruct-2410-Q4_0_4_8.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q4_0_4_8.gguf) | Q4_0_4_8 | 4.66GB | false | Optimized for ARM inference. Requires 'i8mm' support (see link below). *Don't use on Mac or Windows*. |
|
||||
| [Ministral-8B-Instruct-2410-Q4_0_4_4.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q4_0_4_4.gguf) | Q4_0_4_4 | 4.66GB | false | Optimized for ARM inference. Should work well on all ARM chips, pick this if you're unsure. *Don't use on Mac or Windows*. |
|
||||
| [Ministral-8B-Instruct-2410-IQ4_XS.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-IQ4_XS.gguf) | IQ4_XS | 4.45GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
|
||||
| [Ministral-8B-Instruct-2410-Q3_K_L.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q3_K_L.gguf) | Q3_K_L | 4.33GB | false | Lower quality but usable, good for low RAM availability. |
|
||||
| [Ministral-8B-Instruct-2410-Q3_K_M.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q3_K_M.gguf) | Q3_K_M | 4.02GB | false | Low quality. |
|
||||
| [Ministral-8B-Instruct-2410-IQ3_M.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-IQ3_M.gguf) | IQ3_M | 3.79GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
|
||||
| [Ministral-8B-Instruct-2410-Q2_K_L.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q2_K_L.gguf) | Q2_K_L | 3.71GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
|
||||
| [Ministral-8B-Instruct-2410-Q3_K_S.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q3_K_S.gguf) | Q3_K_S | 3.66GB | false | Low quality, not recommended. |
|
||||
| [Ministral-8B-Instruct-2410-IQ3_XS.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-IQ3_XS.gguf) | IQ3_XS | 3.52GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
|
||||
| [Ministral-8B-Instruct-2410-Q2_K.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-Q2_K.gguf) | Q2_K | 3.19GB | false | Very low quality but surprisingly usable. |
|
||||
| [Ministral-8B-Instruct-2410-IQ2_M.gguf](https://huggingface.co/bartowski/Ministral-8B-Instruct-2410-GGUF/blob/main/Ministral-8B-Instruct-2410-IQ2_M.gguf) | IQ2_M | 2.96GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
|
||||
|
||||
## Embed/output weights
|
||||
|
||||
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
|
||||
|
||||
Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.
|
||||
|
||||
Thanks!
|
||||
|
||||
## 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/Ministral-8B-Instruct-2410-GGUF --include "Ministral-8B-Instruct-2410-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/Ministral-8B-Instruct-2410-GGUF --include "Ministral-8B-Instruct-2410-Q8_0/*" --local-dir ./
|
||||
```
|
||||
|
||||
You can either specify a new local-dir (Ministral-8B-Instruct-2410-Q8_0) or download them all in place (./)
|
||||
|
||||
## Q4_0_X_X
|
||||
|
||||
These are *NOT* for Metal (Apple) offloading, only ARM chips.
|
||||
|
||||
If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660)
|
||||
|
||||
To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!).
|
||||
|
||||
## 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.
|
||||
|
||||
If you want to get more into the weeds, you can check out this extremely useful feature chart:
|
||||
|
||||
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
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.
|
||||
|
||||
## Credits
|
||||
|
||||
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset
|
||||
|
||||
Thank you ZeroWw for the inspiration to experiment with embed/output
|
||||
|
||||
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
||||
|
||||
1
configuration.json
Normal file
1
configuration.json
Normal file
@@ -0,0 +1 @@
|
||||
{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
|
||||
Reference in New Issue
Block a user