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MistralLite-7B-GPTQ

Text Generation
Transformers
Safetensors
mistral
text-generation-inference
4-bit precision
gptq
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TheBlokeAI

TheBloke's LLM work is generously supported by a grant from andreessen horowitz (a16z)

MistralLite 7B - GPTQ

Description

This repo contains GPTQ model files for Amazon Web Services's MistralLite 7B.

Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.

Repositories available

Prompt template: Amazon 

<|prompter|>{prompt}</s><|assistant|>

Provided files, and GPTQ parameters

Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.

Each separate quant is in a different branch. See below for instructions on fetching from different branches.

Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.

Explanation of GPTQ parameters
  • Bits: The bit size of the quantised model.
  • GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
  • Act Order: True or False. Also known as desc_act. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
  • Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
  • GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
  • Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
  • ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
Branch Bits GS Act Order Damp % GPTQ Dataset Seq Len Size ExLlama Desc
main 4 128 Yes 0.1 wikitext 4096 4.16 GB Yes 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy.
gptq-4bit-32g-actorder_True 4 32 Yes 0.1 wikitext 4096 4.57 GB Yes 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage.
gptq-8bit--1g-actorder_True 8 None Yes 0.1 wikitext 4096 7.52 GB No 8-bit, with Act Order. No group size, to lower VRAM requirements.
gptq-8bit-128g-actorder_True 8 128 Yes 0.1 wikitext 4096 7.68 GB No 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy.
gptq-8bit-32g-actorder_True 8 32 Yes 0.1 wikitext 4096 8.17 GB No 8-bit, with group size 32g and Act Order for maximum inference quality.
gptq-4bit-64g-actorder_True 4 64 Yes 0.1 wikitext 4096 4.30 GB Yes 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy.

How to download, including from branches

In text-generation-webui

To download from the main branch, enter TheBloke/MistralLite-7B-GPTQ in the "Download model" box.

To download from another branch, add :branchname to the end of the download name, eg TheBloke/MistralLite-7B-GPTQ:gptq-4bit-32g-actorder_True

From the command line

I recommend using the huggingface-hub Python library:

pip3 install huggingface-hub

To download the main branch to a folder called MistralLite-7B-GPTQ:

mkdir MistralLite-7B-GPTQ
huggingface-cli download TheBloke/MistralLite-7B-GPTQ --local-dir MistralLite-7B-GPTQ --local-dir-use-symlinks False

To download from a different branch, add the --revision parameter:

mkdir MistralLite-7B-GPTQ
huggingface-cli download TheBloke/MistralLite-7B-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir MistralLite-7B-GPTQ --local-dir-use-symlinks False
More advanced huggingface-cli download usage

If you remove the --local-dir-use-symlinks False parameter, the files will instead be stored in the central Huggingface cache directory (default location on Linux is: ~/.cache/huggingface), and symlinks will be added to the specified --local-dir, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.

The cache location can be changed with the HF_HOME environment variable, and/or the --cache-dir parameter to huggingface-cli.

For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.

To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:

pip3 install hf_transfer

And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:

mkdir MistralLite-7B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/MistralLite-7B-GPTQ --local-dir MistralLite-7B-GPTQ --local-dir-use-symlinks False

Windows Command Line users: You can set the environment variable by running set HF_HUB_ENABLE_HF_TRANSFER=1 before the download command.

With git (not recommended)

To clone a specific branch with git, use a command like this:

git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/MistralLite-7B-GPTQ

Note that using Git with HF repos is strongly discouraged. It will be much slower than using huggingface-hub, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the .git folder as a blob.)

How to easily download and use this model in text-generation-webui

Please make sure you're using the latest version of text-generation-webui.

It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.

  1. Click the Model tab.

  2. Under Download custom model or LoRA, enter TheBloke/MistralLite-7B-GPTQ.

    • To download from a specific branch, enter for example TheBloke/MistralLite-7B-GPTQ:gptq-4bit-32g-actorder_True
    • see Provided Files above for the list of branches for each option.

  3. Click Download.

  4. The model will start downloading. Once it's finished it will say "Done".

  5. In the top left, click the refresh icon next to Model.

  6. In the Model dropdown, choose the model you just downloaded: MistralLite-7B-GPTQ

  7. The model will automatically load, and is now ready for use!

  8. If you want any custom settings, set them and then click Save settings for this model followed by Reload the Model in the top right.

    • Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file quantize_config.json.

  9. Once you're ready, click the Text Generation tab and enter a prompt to get started!

Serving this model from Text Generation Inference (TGI)

It's recommended to use TGI version 1.1.0 or later. The official Docker container is: ghcr.io/huggingface/text-generation-inference:1.1.0

Example Docker parameters:

--model-id TheBloke/MistralLite-7B-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096

Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):

pip3 install huggingface-hub

from huggingface_hub import InferenceClient

endpoint_url = "https://your-endpoint-url-here"

prompt = "Tell me about AI"
prompt_template=f'''<|prompter|>{prompt}</s><|assistant|>
'''

client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
                                  max_new_tokens=128,
                                  do_sample=True,
                                  temperature=0.7,
                                  top_p=0.95,
                                  top_k=40,
                                  repetition_penalty=1.1)

print(f"Model output: {response}")

How to use this GPTQ model from Python code

Install the necessary packages

Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.

pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/  # Use cu117 if on CUDA 11.7

If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:

pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .

You can then use the following code 

from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

model_name_or_path = "TheBloke/MistralLite-7B-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             trust_remote_code=False,
                                             revision="main")

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

prompt = "Tell me about AI"
prompt_template=f'''<|prompter|>{prompt}</s><|assistant|>
'''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.95,
    top_k=40,
    repetition_penalty=1.1
)

print(pipe(prompt_template)[0]['generated_text'])

Compatibility

The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with Occ4m's GPTQ-for-LLaMa fork.

ExLlama is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.

Huggingface Text Generation Inference (TGI) is compatible with all GPTQ models.

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute

Thanks to the chirper.ai team!

Thanks to Clay from gpus.llm-utils.org!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

Special thanks to: Aemon Algiz.

Patreon special mentions: Pierre Kircher, Stanislav Ovsiannikov, Michael Levine, Eugene Pentland, Andrey, 준교 김, Randy H, Fred von Graf, Artur Olbinski, Caitlyn Gatomon, terasurfer, Jeff Scroggin, James Bentley, Vadim, Gabriel Puliatti, Harry Royden McLaughlin, Sean Connelly, Dan Guido, Edmond Seymore, Alicia Loh, subjectnull, AzureBlack, Manuel Alberto Morcote, Thomas Belote, Lone Striker, Chris Smitley, Vitor Caleffi, Johann-Peter Hartmann, Clay Pascal, biorpg, Brandon Frisco, sidney chen, transmissions 11, Pedro Madruga, jinyuan sun, Ajan Kanaga, Emad Mostaque, Trenton Dambrowitz, Jonathan Leane, Iucharbius, usrbinkat, vamX, George Stoitzev, Luke Pendergrass, theTransient, Olakabola, Swaroop Kallakuri, Cap'n Zoog, Brandon Phillips, Michael Dempsey, Nikolai Manek, danny, Matthew Berman, Gabriel Tamborski, alfie_i, Raymond Fosdick, Tom X Nguyen, Raven Klaugh, LangChain4j, Magnesian, Illia Dulskyi, David Ziegler, Mano Prime, Luis Javier Navarrete Lozano, Erik Bjäreholt, 阿明, Nathan Dryer, Alex, Rainer Wilmers, zynix, TL, Joseph William Delisle, John Villwock, Nathan LeClaire, Willem Michiel, Joguhyik, GodLy, OG, Alps Aficionado, Jeffrey Morgan, ReadyPlayerEmma, Tiffany J. Kim, Sebastain Graf, Spencer Kim, Michael Davis, webtim, Talal Aujan, knownsqashed, John Detwiler, Imad Khwaja, Deo Leter, Jerry Meng, Elijah Stavena, Rooh Singh, Pieter, SuperWojo, Alexandros Triantafyllidis, Stephen Murray, Ai Maven, ya boyyy, Enrico Ros, Ken Nordquist, Deep Realms, Nicholas, Spiking Neurons AB, Elle, Will Dee, Jack West, RoA, Luke @flexchar, Viktor Bowallius, Derek Yates, Subspace Studios, jjj, Toran Billups, Asp the Wyvern, Fen Risland, Ilya, NimbleBox.ai, Chadd, Nitin Borwankar, Emre, Mandus, Leonard Tan, Kalila, K, Trailburnt, S_X, Cory Kujawski

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: Amazon Web Services's MistralLite 7B

MistralLite Model

MistralLite is a fine-tuned Mistral-7B-v0.1 language model, with enhanced capabilities of processing long context (up to 32K tokens). By utilizing an adapted Rotary Embedding and sliding window during fine-tuning, MistralLite is able to perform significantly better on several long context retrieve and answering tasks, while keeping the simple model structure of the original model. MistralLite is useful for applications such as long context line and topic retrieval, summarization, question-answering, and etc. MistralLite can be deployed on a single AWS g5.2x instance with Sagemaker Huggingface Text Generation Inference (TGI) endpoint, making it suitable for applications that require high performance in resource-constrained environments. You can also serve the MistralLite model directly using TGI docker containers. Also, MistralLite supports other ways of serving like vLLM, and you can use MistralLite in Python by using the HuggingFace transformers and FlashAttention-2 library.

MistralLite is similar to Mistral-7B-Instruct-v0.1, and their similarities and differences are summarized below:

Model Fine-tuned on long contexts Max context length RotaryEmbedding adaptation Sliding Window Size
Mistral-7B-Instruct-v0.1 up to 8K tokens 32K rope_theta = 10000 4096
MistralLite up to 16K tokens 32K rope_theta = 1000000 16384

Motivation of Developing MistralLite

Since the release of Mistral-7B-Instruct-v0.1, the model became increasingly popular because its strong performance on a wide range of benchmarks. But most of the benchmarks are evaluated on short context, and not much has been investigated on its performance on long context tasks. Then We evaluated Mistral-7B-Instruct-v0.1 against benchmarks that are specifically designed to assess the capabilities of LLMs in handling longer context. Although the performance of the models on long context was fairly competitive on long context less than 4096 tokens, there were some limitations on its performance on longer context. Motivated by improving its performance on longer context, we finetuned the Mistral 7B model, and produced Mistrallite. The model managed to significantly boost the performance of long context handling over Mistral-7B-Instruct-v0.1. The detailed long context evalutaion results are as below:

  1. Topic Retrieval
    Model Name Input length Input length Input length Input length Input length
    2851 5568 8313 11044 13780

| Mistral-7B-Instruct-v0.1 | 100% | 50% | 2% | 0% | 0% | | MistralLite | 100% | 100% | 100% | 100% | 98% |

  1. Line Retrieval
Model Name Input length Input length Input length Input length Input length Input length
3818 5661 7505 9354 11188 12657
Mistral-7B-Instruct-v0.1 98% 62% 42% 42% 32% 30%
MistralLite 98% 92% 88% 76% 70% 60%
  1. Pass key Retrieval
Model Name Input length Input length Input length Input length
3264 5396 8329 10197
Mistral-7B-Instruct-v0.1 100% 50% 20% 30%
MistralLite 100% 100% 100% 100%
  1. Question Answering with Long Input Texts
    Model Name Test set Accuracy Hard subset Accuracy
    Mistral-7B-Instruct-v0.1 44.3% 39.7%
    MistralLite 64.4% 56.2%

Example test on long context of 13400 tokens

  • Context is from Amazon Aurora FAQs
  • Question: please tell me how does pgvector help with Generative AI and give me some examples.
  • Answer from MistralLite:
    pgvector is an open-source extension for PostgreSQL supported by Amazon Aurora PostgreSQL-Compatible Edition.

You can use pgvector to store, search, index, and query billions of embeddings that are generated from machine learning (ML) and artificial intelligence (AI) models in your database, such as those from Amazon Bedrock (limited preview) or Amazon SageMaker. A vector embedding is a numerical representation that represents the semantic meaning of content such as text, images, and video.

With pgvector, you can query embeddings in your Aurora PostgreSQL database to perform efficient semantic similarity searches of these data types, represented as vectors, combined with other tabular data in Aurora. This enables the use of generative AI and other AI/ML systems for new types of applications such as personalized recommendations based on similar text descriptions or images, candidate match based on interview notes, customer service next best action recommendations based on successful transcripts or chat session dialogs, and more.

Model Details

How to Use MistralLite from Python Code (HuggingFace transformers)

Important - For an end-to-end example Jupyter notebook, please refer to this link.

Install the necessary packages

Requires: transformers 4.34.0 or later, flash-attn 2.3.1.post1 or later, and accelerate 0.23.0 or later.

pip install transformers==4.34.0
pip install flash-attn==2.3.1.post1 --no-build-isolation
pip install accelerate==0.23.0

You can then try the following example code 

from transformers import AutoModelForCausalLM, AutoTokenizer
import transformers
import torch

model_id = "amazon/MistralLite"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
                                             torch_dtype=torch.bfloat16,
                                             use_flash_attention_2=True,
                                             device_map="auto",)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
)
prompt = "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>"

sequences = pipeline(
    prompt,
    max_new_tokens=400,
    do_sample=False,
    return_full_text=False,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
    print(f"{seq['generated_text']}")

Important - Use the prompt template below for MistralLite:

<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>

How to Serve MistralLite on TGI

Important:

  • For an end-to-end example Jupyter notebook using the native TGI container, please refer to this link.
  • If the input context length is greater than 12K tokens, it is recommended using a custom TGI container, please refer to this link.

Start TGI server

Use TGI version 1.1.0 or later. The official Docker container is: ghcr.io/huggingface/text-generation-inference:1.1.0

Example Docker parameters:

docker run -d --gpus all --shm-size 1g -p 443:80 -v $(pwd)/models:/data ghcr.io/huggingface/text-generation-inference:1.1.0 \
      --model-id amazon/MistralLite \
      --max-input-length 16000 \
      --max-total-tokens 16384 \
      --max-batch-prefill-tokens 16384 \
      --trust-remote-code

Perform Inference

Example Python code for inference with TGI (requires text_generation 0.6.1 or later):

pip install text_generation==0.6.1

from text_generation import Client

SERVER_PORT = 443
SERVER_HOST = "localhost"
SERVER_URL = f"{SERVER_HOST}:{SERVER_PORT}"
tgi_client = Client(f"http://{SERVER_URL}", timeout=60)

def invoke_tgi(prompt, 
                      random_seed=1, 
                      max_new_tokens=400, 
                      print_stream=True,
                      assist_role=True):
    if (assist_role):
        prompt = f"<|prompter|>{prompt}</s><|assistant|>"
    output = ""
    for response in tgi_client.generate_stream(
        prompt,
        do_sample=False,
        max_new_tokens=max_new_tokens,
        return_full_text=False,
        #temperature=None,
        #truncate=None,
        #seed=random_seed,
        #typical_p=0.2,
    ):
        if hasattr(response, "token"):
            if not response.token.special:
                snippet = response.token.text
                output += snippet
                if (print_stream):
                    print(snippet, end='', flush=True)
    return output

prompt = "What are the main challenges to support a long context for LLM?"
result = invoke_tgi(prompt)

Important - When using MistralLite for inference for the first time, it may require a brief 'warm-up' period that can take 10s of seconds. However, subsequent inferences should be faster and return results in a more timely manner. This warm-up period is normal and should not affect the overall performance of the system once the initialisation period has been completed.

How to Deploy MistralLite on Amazon SageMaker

Important:

  • For an end-to-end example Jupyter notebook using the SageMaker built-in container, please refer to this link.
  • If the input context length is greater than 12K tokens, it is recommended using a custom docker container, please refer to this link.

Install the necessary packages

Requires: sagemaker 2.192.1 or later.

pip install sagemaker==2.192.1

Deploy the Model as A SageMaker Endpoint

To deploy MistralLite on a SageMaker endpoint, please follow the example code as below.

import sagemaker
from sagemaker.huggingface import HuggingFaceModel, get_huggingface_llm_image_uri
import time

sagemaker_session = sagemaker.Session()
region = sagemaker_session.boto_region_name
role = sagemaker.get_execution_role()

image_uri = get_huggingface_llm_image_uri(
  backend="huggingface", # or lmi
  region=region,
 version="1.1.0"
)

model_name = "MistralLite-" + time.strftime("%Y-%m-%d-%H-%M-%S", time.gmtime())

hub = {
    'HF_MODEL_ID':'amazon/MistralLite',
    'HF_TASK':'text-generation',
    'SM_NUM_GPUS':'1',
    "MAX_INPUT_LENGTH": '16000',
    "MAX_TOTAL_TOKENS": '16384',
    "MAX_BATCH_PREFILL_TOKENS": '16384',
    "MAX_BATCH_TOTAL_TOKENS":  '16384',
}

model = HuggingFaceModel(
    name=model_name,
    env=hub,
    role=role,
    image_uri=image_uri
)
predictor = model.deploy(
  initial_instance_count=1,
  instance_type="ml.g5.2xlarge",
  endpoint_name=model_name,
    
)

Perform Inference

To call the endpoint, please follow the example code as below:

input_data = {
  "inputs": "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",
  "parameters": {
    "do_sample": False,
    "max_new_tokens": 400,
    "return_full_text": False,
    #"typical_p": 0.2,
    #"temperature":None,
    #"truncate":None,
    #"seed": 1,
  }
}
result = predictor.predict(input_data)[0]["generated_text"]
print(result)

or via boto3, and the example code is shown as below:

import boto3
import json
def call_endpoint(client, prompt, endpoint_name, paramters):
    client = boto3.client("sagemaker-runtime")
    payload = {"inputs": prompt,
               "parameters": parameters}
    response = client.invoke_endpoint(EndpointName=endpoint_name,
                                      Body=json.dumps(payload), 
                                      ContentType="application/json")
    output = json.loads(response["Body"].read().decode())
    result = output[0]["generated_text"]
    return result

client = boto3.client("sagemaker-runtime")
parameters = {
    "do_sample": False,
    "max_new_tokens": 400,
    "return_full_text": False,
    #"typical_p": 0.2,
    #"temperature":None,
    #"truncate":None,
    #"seed": 1,
}
endpoint_name = predictor.endpoint_name
prompt = "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>"
result = call_endpoint(client, prompt, endpoint_name, parameters)
print(result)

How to Serve MistralLite on vLLM

Documentation on installing and using vLLM can be found here.

Important - For an end-to-end example Jupyter notebook, please refer to this link.

Using vLLM as a server

When using vLLM as a server, pass the --model amazon/MistralLite parameter, for example:

python3 -m vllm.entrypoints.api_server --model amazon/MistralLite

Using vLLM in Python Code

When using vLLM from Python code, Please see the example code as below:

from vllm import LLM, SamplingParams

prompts = [
   "<|prompter|>What are the main challenges to support a long context for LLM?</s><|assistant|>",
]
sampling_params = SamplingParams(temperature=0, max_tokens=100)

llm = LLM(model="amazon/MistralLite",)

outputs = llm.generate(prompts, sampling_params)

# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")

Limitations

Before using the MistralLite model, it is important to perform your own independent assessment, and take measures to ensure that your use would comply with your own specific quality control practices and standards, and that your use would comply with the local rules, laws, regulations, licenses and terms that apply to you, and your content.

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