metadata
quantized_by: bartowski
pipeline_tag: text-generation
license_name: mrl
extra_gated_button_content: Submit
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: https://mistral.ai/contact/
## 1. Scope and acceptance
**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.
**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.
**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.
## 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>.*
license: other
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
language:
- en
- fr
- de
- es
- it
- pt
- zh
- ja
- ru
- ko
license_link: https://mistral.ai/licenses/MRL-0.1.md
inference: false
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>.
base_model: mistralai/Mistral-Large-Instruct-2411
Llamacpp imatrix Quantizations of Mistral-Large-Instruct-2411
Using llama.cpp release b4058 for quantization.
Original model: https://huggingface.co/mistralai/Mistral-Large-Instruct-2411
All quants made using imatrix option with dataset from here
Run them in LM Studio
Prompt format
<s>[SYSTEM_PROMPT] {system_prompt}[/SYSTEM_PROMPT][INST] {prompt}[/INST]
Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
|---|---|---|---|---|
| Mistral-Large-Instruct-2411-Q8_0.gguf | Q8_0 | 130.28GB | true | Extremely high quality, generally unneeded but max available quant. |
| Mistral-Large-Instruct-2411-Q6_K.gguf | Q6_K | 100.59GB | true | Very high quality, near perfect, recommended. |
| Mistral-Large-Instruct-2411-Q5_K_M.gguf | Q5_K_M | 86.49GB | true | High quality, recommended. |
| Mistral-Large-Instruct-2411-Q5_K_S.gguf | Q5_K_S | 84.36GB | true | High quality, recommended. |
| Mistral-Large-Instruct-2411-Q4_K_M.gguf | Q4_K_M | 73.22GB | true | Good quality, default size for most use cases, recommended. |
| Mistral-Large-Instruct-2411-Q4_K_S.gguf | Q4_K_S | 69.57GB | true | Slightly lower quality with more space savings, recommended. |
| Mistral-Large-Instruct-2411-Q4_0.gguf | Q4_0 | 69.32GB | true | Legacy format, generally not worth using over similarly sized formats |
| Mistral-Large-Instruct-2411-Q4_0_8_8.gguf | Q4_0_8_8 | 69.08GB | true | Optimized for ARM and AVX inference. Requires 'sve' support for ARM (see details below). Don't use on Mac. |
| Mistral-Large-Instruct-2411-IQ4_XS.gguf | IQ4_XS | 65.43GB | true | Decent quality, smaller than Q4_K_S with similar performance, recommended. |
| Mistral-Large-Instruct-2411-Q3_K_XL.gguf | Q3_K_XL | 64.91GB | true | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| Mistral-Large-Instruct-2411-Q3_K_L.gguf | Q3_K_L | 64.55GB | true | Lower quality but usable, good for low RAM availability. |
| Mistral-Large-Instruct-2411-Q3_K_M.gguf | Q3_K_M | 59.10GB | true | Low quality. |
| Mistral-Large-Instruct-2411-IQ3_M.gguf | IQ3_M | 55.28GB | true | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| Mistral-Large-Instruct-2411-Q3_K_S.gguf | Q3_K_S | 52.85GB | true | Low quality, not recommended. |
| Mistral-Large-Instruct-2411-IQ3_XXS.gguf | IQ3_XXS | 47.01GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| Mistral-Large-Instruct-2411-Q2_K_L.gguf | Q2_K_L | 45.59GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| Mistral-Large-Instruct-2411-Q2_K.gguf | Q2_K | 45.20GB | false | Very low quality but surprisingly usable. |
| Mistral-Large-Instruct-2411-IQ2_M.gguf | IQ2_M | 41.62GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
| Mistral-Large-Instruct-2411-IQ2_XS.gguf | IQ2_XS | 36.08GB | false | Low quality, uses SOTA techniques to be usable. |
| Mistral-Large-Instruct-2411-IQ2_XXS.gguf | IQ2_XXS | 32.43GB | false | Very low quality, uses SOTA techniques to be usable. |
| Mistral-Large-Instruct-2411-IQ1_M.gguf | IQ1_M | 28.39GB | false | Extremely low quality, not recommended. |
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.
Downloading using huggingface-cli
Click to view download instructions
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/Mistral-Large-Instruct-2411-GGUF --include "Mistral-Large-Instruct-2411-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-2411-GGUF --include "Mistral-Large-Instruct-2411-Q8_0/*" --local-dir ./
You can either specify a new local-dir (Mistral-Large-Instruct-2411-Q8_0) or download them all in place (./)
Q4_0_X_X information
These are NOT for Metal (Apple) or GPU (nvidia/AMD/intel) offloading, only ARM chips (and certain AVX2/AVX512 CPUs).
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
To check which one would work best for your ARM chip, you can check AArch64 SoC features (thanks EloyOn!).
If you're using a CPU that supports AVX2 or AVX512 (typically server CPUs and AMD's latest Zen5 CPUs) and are not offloading to a GPU, the Q4_0_8_8 may offer a nice speed as well:
Click to view benchmarks on an AVX2 system (EPYC7702)
| model | size | params | backend | threads | test | t/s | % (vs Q4_0) |
|---|---|---|---|---|---|---|---|
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% |
Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation
Which file should I choose?
Click here for details
A great write up with charts showing various performances is provided by Artefact2 here
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:
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