alt.freeweights.mistral / Mistral-Small-3.2-24B-Instruct-2506-GGUF
From: unsloth <huggingface.co/unsloth>Newsgroups: alt.freeweights.mistralSubject:

Mistral-Small-3.2-24B-Instruct-2506-GGUF

Date: 2026-11-15Commit: b750ec2Signed: 2DC28CC7B26A283B ✓

Image text to text model. Gguf weights, 389 GB in all. Published by unsloth under Apache-2.0.

mistral389GBMistral-Small-3.2-24B-Instruct-2506-GGUFggufpermissive
FactsParamsnot statedSize389 GBFormatsggufTaskimage text to textLicenseApache-2.0From sourcepermissive
$ fw get unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF
# manifest sha256 b192f1a00bc9… · 389 GB · 35 files
$ fw verify ./Mistral-Small-3.2-24B-Instruct-2506-GGUF unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF
From sourceHosted (signed)unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUFLast check: 2026-11-15 04:36 UTC

Get help: custom fine-tune or private deploy. We review your setup before we quote.

Quantized of Mistral-Small-3.2-24B-Instruct-2506.

Works with

Convert with

Overview

Size
389 GB
Weights
5.56 GB (smallest of 26 GGUF variants)
Parameters
not stated
Formats
gguf
Task
image text to text
License
Apache-2.0 (permissive)
Files
35, each with a sha256
Publisher
unsloth
Added
2026-11-15
Section
alt.freeweights.mistral
Lineage

Original release from the publisher.

Base model: mistralai/Mistral-Small-3.2-24B-Instruct-2506

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Model card

From the publisher at commit b750ec2. Shown as plain text with links; images open at the publisher.

Note. Includes our GGUF chat template fixes! Tool calling works as well!

If you are using llama.cpp, use --jinja to enable the system prompt.

Unsloth Dynamic 2.0 achieves SOTA performance in model quantization.

https://github.com/unslothai/unsloth/ https://discord.gg/unsloth https://www.unsloth.ai/blog/mistral-small-3.1

✨ How to Use Mistral 3.2 Small:#

Run in llama.cpp:

./llama.cpp/llama-cli -hf unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF:UD-Q4_K_XL --jinja --temp 0.15 --top-k -1 --top-p 1.00 -ngl 99

Run in Ollama:

ollama run hf.co/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF:UD-Q4_K_XL
  • Temperature of: 0.15
  • Set top_p to: 1.00
  • Max tokens (context length): 128K
  • Fine-tune Mistral v0.3 (7B) for free using our Google Colab notebook here-Conversational.ipynb)!
  • View the rest of our notebooks in our docs here.

Mistral-Small-3.2-24B-Instruct-2506#

Mistral-Small-3.2-24B-Instruct-2506 is a minor update of Mistral-Small-3.1-24B-Instruct-2503.

Small-3.2 improves in the following categories:

  • Instruction following: Small-3.2 is better at following precise instructions
  • Repetition errors: Small-3.2 produces less infinite generations or repetitive answers
  • Function calling: Small-3.2's function calling template is more robust (see here and examples)

In all other categories Small-3.2 should match or slightly improve compared to Mistral-Small-3.1-24B-Instruct-2503.

Key Features#

Benchmark Results#

We compare Mistral-Small-3.2-24B to Mistral-Small-3.1-24B-Instruct-2503. For more comparison against other models of similar size, please check Mistral-Small-3.1's Benchmarks'

Text#
Instruction Following / Chat / Tone#
ModelWildbench v2Arena Hard v2IF (Internal; accuracy)
Small 3.1 24B Instruct55.6%19.56%82.75%
Small 3.2 24B Instruct65.33%43.1%84.78%
Infinite Generations#

Small 3.2 reduces infitine generations by 2x on challenging, long and repetitive prompts.

ModelInfinite Generations (Internal; Lower is better)
Small 3.1 24B Instruct2.11%
Small 3.2 24B Instruct1.29%
STEM#
ModelMMLUMMLU Pro (5-shot CoT)MATHGPQA Main (5-shot CoT)GPQA Diamond (5-shot CoT )MBPP Plus - Pass@5HumanEval Plus - Pass@5SimpleQA (TotalAcc)
Small 3.1 24B Instruct80.62%66.76%69.30%44.42%45.96%74.63%88.99%10.43%
Small 3.2 24B Instruct80.50%69.06%69.42%44.22%46.13%78.33%92.90%12.10%
Vision#
ModelMMMUMathvistaChartQADocVQAAI2D
Small 3.1 24B Instruct64.00%68.91%86.24%94.08%93.72%
Small 3.2 24B Instruct62.50%67.09%87.4%94.86%92.91%

Usage#

The model can be used with the following frameworks;

Note 1: We recommend using a relatively low temperature, such as temperature=0.15.

Note 2: Make sure to add a system prompt to the model to best tailer it for your needs. If you want to use the model as a general assistant, we recommend to use the one provided in the SYSTEM_PROMPT.txt file.

We recommend using this model with vLLM.

Installation#

Make sure to install vLLM >= 0.9.1:

pip install vllm --upgrade

Doing so should automatically install mistral_common >= 1.6.2.

To check:

python -c "import mistral_common; print(mistral_common.__version__)"

You can also make use of a ready-to-go docker image or on the docker hub.

Serve#

We recommand that you use Mistral-Small-3.2-24B-Instruct-2506 in a server/client setting.

  1. Spin up a server:
vllm serve mistralai/Mistral-Small-3.2-24B-Instruct-2506 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --limit_mm_per_prompt 'image=10' --tensor-parallel-size 2

Note: Running Mistral-Small-3.2-24B-Instruct-2506 on GPU requires ~55 GB of GPU RAM in bf16 or fp16.

  1. To ping the client you can use a simple Python snippet. See the following examples.
Vision reasoning#

Take leverage of the vision capabilities of Mistral-Small-3.2-24B-Instruct-2506 to take the best choice given a scenario, go catch them all !

Python snippet

from datetime import datetime, timedelta

from openai import OpenAI
from huggingface_hub import hf_hub_download

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

TEMP = 0.15
MAX_TOK = 131072

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id


def load_system_prompt(repo_id: str, filename: str) -> str:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, "r") as file:
        system_prompt = file.read()
    today = datetime.today().strftime("%Y-%m-%d")
    yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
    model_name = repo_id.split("/")[-1]
    return system_prompt.format(name=model_name, today=today, yesterday=yesterday)


model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.",
            },
            {"type": "image_url", "image_url": {"url": image_url}},
        ],
    },
]


response = client.chat.completions.create(
    model=model,
    messages=messages,
    temperature=TEMP,
    max_tokens=MAX_TOK,
)

print(response.choices[0].message.content)
# In this situation, you are playing a Pokémon game where your Pikachu (Level 42) is facing a wild Pidgey (Level 17). Here are the possible actions you can take and an analysis of each:

# 1. **FIGHT**:
#    - **Pros**: Pikachu is significantly higher level than the wild Pidgey, which suggests that it should be able to defeat Pidgey easily. This could be a good opportunity to gain experience points and possibly items or money.
#    - **Cons**: There is always a small risk of Pikachu fainting, especially if Pidgey has a powerful move or a status effect that could hinder Pikachu. However, given the large level difference, this risk is minimal.

# 2. **BAG**:
#    - **Pros**: You might have items in your bag that could help in this battle, such as Potions, Poké Balls, or Berries. Using an item could help you capture the Pidgey or heal your Pikachu if needed.
#    - **Cons**: Using items might not be necessary given the level difference. It could be more efficient to just fight and defeat the Pidgey quickly.

# 3. **POKÉMON**:
#    - **Pros**: You might have another Pokémon in your party that is better suited for this battle or that you want to gain experience. Switching Pokémon could also be a strategic move if you want to train a lower-level Pokémon.
#    - **Cons**: Switching Pokémon might not be necessary since Pikachu is at a significant advantage. It could also waste time and potentially give Pidgey a turn to attack.

# 4. **RUN**:
#    - **Pros**: Running away could save time and conserve your Pokémon's health and resources. If you are in a hurry or do not need the experience or items, running away is a safe option.
#    - **Cons**: Running away means you miss out on the experience points and potential items or money that you could gain from defeating the Pidgey. It also means you do not get the chance to capture the Pidgey if you wanted to.

# ### Recommendation:
# Given the significant level advantage, the best action is likely to **FIGHT**. This will allow you to quickly defeat the Pidgey, gain experience points, and potentially earn items or money. If you are concerned about Pikachu's health, you could use an item from your **BAG** to heal it before or during the battle. Running away or switching Pokémon does not seem necessary in this situation.
Function calling#

Mistral-Small-3.2-24B-Instruct-2506 is excellent at function / tool calling tasks via vLLM. E.g.:

Python snippet - easy

from openai import OpenAI
from huggingface_hub import hf_hub_download

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

TEMP = 0.15
MAX_TOK = 131072

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id

def load_system_prompt(repo_id: str, filename: str) -> str:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, "r") as file:
        system_prompt = file.read()
    return system_prompt

model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")

image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png"

tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_population",
            "description": "Get the up-to-date population of a given country.",
            "parameters": {
                "type": "object",
                "properties": {
                    "country": {
                        "type": "string",
                        "description": "The country to find the population of.",
                    },
                    "unit": {
                        "type": "string",
                        "description": "The unit for the population.",
                        "enum": ["millions", "thousands"],
                    },
                },
                "required": ["country", "unit"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "rewrite",
            "description": "Rewrite a given text for improved clarity",
            "parameters": {
                "type": "object",
                "properties": {
                    "text": {
                        "type": "string",
                        "description": "The input text to rewrite",
                    }
                },
            },
        },
    },
]

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {
        "role": "user",
        "content": "Could you please make the below article more concise?\n\nOpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership.",
    },
    {
        "role": "assistant",
        "content": "",
        "tool_calls": [
            {
                "id": "bbc5b7ede",
                "type": "function",
                "function": {
                    "name": "rewrite",
                    "arguments": '{"text": "OpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership."}',
                },
            }
        ],
    },
    {
        "role": "tool",
        "content": '{"action":"rewrite","outcome":"OpenAI is a FOR-profit company."}',
        "tool_call_id": "bbc5b7ede",
        "name": "rewrite",
    },
    {
        "role": "assistant",
        "content": "---\n\nOpenAI is a FOR-profit company.",
    },
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Can you tell me what is the biggest country depicted on the map?",
            },
            {
                "type": "image_url",
                "image_url": {
                    "url": image_url,
                },
            },
        ],
    }
]

response = client.chat.completions.create(
    model=model,
    messages=messages,
    temperature=TEMP,
    max_tokens=MAX_TOK,
    tools=tools,
    tool_choice="auto",
)

assistant_message = response.choices[0].message.content
print(assistant_message)
# The biggest country depicted on the map is Russia.

messages.extend([
    {"role": "assistant", "content": assistant_message},
    {"role": "user", "content": "What is the population of that country in millions?"},
])

response = client.chat.completions.create(
    model=model,
    messages=messages,
    temperature=TEMP,
    max_tokens=MAX_TOK,
    tools=tools,
    tool_choice="auto",
)

print(response.choices[0].message.tool_calls)
# [ChatCompletionMessageToolCall(id='3e92V6Vfo', function=Function(arguments='{"country": "Russia", "unit": "millions"}', name='get_current_population'), type='function')]

Python snippet - complex

import json
from openai import OpenAI
from huggingface_hub import hf_hub_download

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

TEMP = 0.15
MAX_TOK = 131072

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id


def load_system_prompt(repo_id: str, filename: str) -> str:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, "r") as file:
        system_prompt = file.read()
    return system_prompt


model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")

image_url = "https://math-coaching.com/img/fiche/46/expressions-mathematiques.jpg"


def my_calculator(expression: str) -> str:
    return str(eval(expression))


tools = [
    {
        "type": "function",
        "function": {
            "name": "my_calculator",
            "description": "A calculator that can evaluate a mathematical expression.",
            "parameters": {
                "type": "object",
                "properties": {
                    "expression": {
                        "type": "string",
                        "description": "The mathematical expression to evaluate.",
                    },
                },
                "required": ["expression"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "rewrite",
            "description": "Rewrite a given text for improved clarity",
            "parameters": {
                "type": "object",
                "properties": {
                    "text": {
                        "type": "string",
                        "description": "The input text to rewrite",
                    }
                },
            },
        },
    },
]

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Can you calculate the results for all the equations displayed in the image? Only compute the ones that involve numbers.",
            },
            {
                "type": "image_url",
                "image_url": {
                    "url": image_url,
                },
            },
        ],
    },
]

response = client.chat.completions.create(
    model=model,
    messages=messages,
    temperature=TEMP,
    max_tokens=MAX_TOK,
    tools=tools,
    tool_choice="auto",
)

tool_calls = response.choices[0].message.tool_calls
print(tool_calls)
# [ChatCompletionMessageToolCall(id='CyQBSAtGh', function=Function(arguments='{"expression": "6 + 2 * 3"}', name='my_calculator'), type='function'), ChatCompletionMessageToolCall(id='KQqRCqvzc', function=Function(arguments='{"expression": "19 - (8 + 2) + 1"}', name='my_calculator'), type='function')]

results = []
for tool_call in tool_calls:
    function_name = tool_call.function.name
    function_args = tool_call.function.arguments
    if function_name == "my_calculator":
        result = my_calculator(**json.loads(function_args))
        results.append(result)

messages.append({"role": "assistant", "tool_calls": tool_calls})
for tool_call, result in zip(tool_calls, results):
    messages.append(
        {
            "role": "tool",
            "tool_call_id": tool_call.id,
            "name": tool_call.function.name,
            "content": result,
        }
    )


response = client.chat.completions.create(
    model=model,
    messages=messages,
    temperature=TEMP,
    max_tokens=MAX_TOK,
)

print(response.choices[0].message.content)
# Here are the results for the equations that involve numbers:

# 1. \( 6 + 2 \times 3 = 12 \)
# 3. \( 19 - (8 + 2) + 1 = 10 \)

# For the other equations, you need to substitute the variables with specific values to compute the results.
Instruction following#

Mistral-Small-3.2-24B-Instruct-2506 will follow your instructions down to the last letter !

Python snippet

from openai import OpenAI
from huggingface_hub import hf_hub_download

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

TEMP = 0.15
MAX_TOK = 131072

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

models = client.models.list()
model = models.data[0].id


def load_system_prompt(repo_id: str, filename: str) -> str:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, "r") as file:
        system_prompt = file.read()
    return system_prompt


model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {
        "role": "user",
        "content": "Write me a sentence where every word starts with the next letter in the alphabet - start with 'a' and end with 'z'.",
    },
]

response = client.chat.completions.create(
    model=model,
    messages=messages,
    temperature=TEMP,
    max_tokens=MAX_TOK,
)

assistant_message = response.choices[0].message.content
print(assistant_message)

# Here's a sentence where each word starts with the next letter of the alphabet, starting from 'a' and ending with 'z':

# "Always brave cats dance elegantly, fluffy giraffes happily ignore jungle kites, lovingly munching nuts, observing playful quails racing swiftly, tiny unicorns vaulting while xylophones yodel zealously."

# This sentence follows the sequence from A to Z without skipping any letters.
Transformers#

You can also use Mistral-Small-3.2-24B-Instruct-2506 with Transformers !

To make the best use of our model with Transformers make sure to have installed mistral-common >= 1.6.2 to use our tokenizer.

pip install mistral-common --upgrade

Then load our tokenizer along with the model and generate:

Python snippet

from datetime import datetime, timedelta
import torch

from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from huggingface_hub import hf_hub_download
from transformers import Mistral3ForConditionalGeneration


def load_system_prompt(repo_id: str, filename: str) -> str:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, "r") as file:
        system_prompt = file.read()
    today = datetime.today().strftime("%Y-%m-%d")
    yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
    model_name = repo_id.split("/")[-1]
    return system_prompt.format(name=model_name, today=today, yesterday=yesterday)


model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")

tokenizer = MistralTokenizer.from_hf_hub(model_id)

model = Mistral3ForConditionalGeneration.from_pretrained(
    model_id, torch_dtype=torch.bfloat16
)

image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.",
            },
            {"type": "image_url", "image_url": {"url": image_url}},
        ],
    },
]

tokenized = tokenizer.encode_chat_completion(ChatCompletionRequest(messages=messages))

input_ids = torch.tensor([tokenized.tokens])
attention_mask = torch.ones_like(input_ids)
pixel_values = torch.tensor(tokenized.images[0], dtype=torch.bfloat16).unsqueeze(0)
image_sizes = torch.tensor([pixel_values.shape[-2:]])

output = model.generate(
    input_ids=input_ids,
    attention_mask=attention_mask,
    pixel_values=pixel_values,
    image_sizes=image_sizes,
    max_new_tokens=1000,
)[0]

decoded_output = tokenizer.decode(output[len(tokenized.tokens) :])
print(decoded_output)
# In this situation, you are playing a Pokémon game where your Pikachu (Level 42) is facing a wild Pidgey (Level 17). Here are the possible actions you can take and an analysis of each:

# 1. **FIGHT**:
#    - **Pros**: Pikachu is significantly higher level than the wild Pidgey, which suggests that it should be able to defeat Pidgey easily. This could be a good opportunity to gain experience points and possibly items or money.
#    - **Cons**: There is always a small risk of Pikachu fainting, especially if Pidgey has a powerful move or a status effect that could hinder Pikachu. However, given the large level difference, this risk is minimal.

# 2. **BAG**:
#    - **Pros**: You might have items in your bag that could help in this battle, such as Potions, Poké Balls, or Berries. Using an item could help you capture Pidgey or heal Pikachu if needed.
#    - **Cons**: Using items might not be necessary given the level difference. It could be more efficient to just fight and defeat Pidgey quickly.

# 3. **POKÉMON**:
#    - **Pros**: You might have another Pokémon in your party that is better suited for this battle or that you want to gain experience. Switching Pokémon could also be strategic if you want to train a lower-level Pokémon.
#    - **Cons**: Switching Pokémon might not be necessary since Pikachu is at a significant advantage. It could also waste time and potentially give Pidgey a turn to attack.

# 4. **RUN**:
#    - **Pros**: Running away could be a quick way to avoid the battle altogether. This might be useful if you are trying to conserve resources or if you are in a hurry to get to another location.
#    - **Cons**: Running away means you miss out on the experience points, items, or money that you could gain from defeating Pidgey. It also might not be the most efficient use of your time if you are trying to train your Pokémon.

# ### Recommendation:
# Given the significant level advantage, the best action to take is likely **FIGHT**. This will allow you to quickly defeat Pidgey and gain experience points for Pikachu. If you are concerned about Pikachu's health, you could use the **BAG** to heal Pikachu before or during the battle. Running away or switching Pokémon does not seem necessary in this situation.

Files

35 files · 389 GBWeights 5.56 GB (smallest of 26 GGUF variants). Sizes and sha256 from the signed manifest.
Show all 35 files with their sha256
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.gitattributes4.00 KB33d3bbb60c1a380a68ae0670f5afd6417d42b22366f7632e9af672b382a2332b
Mistral-Small-3.2-24B-Instruct-2506-BF16.gguf47.2 GB2cf2c40be36a362473e31ed2334492dc324731ced89070e3c16c0bfba209be5b
Mistral-Small-3.2-24B-Instruct-2506-IQ4_NL.gguf13.5 GB6403b553c45bfce9102ffdce32ec6160b91ce73340926767939978bc2ebdeb2c
Mistral-Small-3.2-24B-Instruct-2506-IQ4_XS.gguf12.8 GBd138036a144c55a39275429fb7a37c204fa2291814972768a400d810d6c4c5aa
Mistral-Small-3.2-24B-Instruct-2506-Q2_K.gguf8.89 GBc53491f0f90f4cd6d54dac92f0942441fa79e40149d3e3b573f66fb0d7df34e7
Mistral-Small-3.2-24B-Instruct-2506-Q2_K_L.gguf9.05 GB7e2231f79b7c1b1d0af357afeaa8608efc7db4509ef5ac0845ffe7e82baf7e37
Mistral-Small-3.2-24B-Instruct-2506-Q3_K_M.gguf11.5 GB830d8937494e80e64b0ab03ed69a7f29b80dbe5c4d0656517be6620f231a3fdd
Mistral-Small-3.2-24B-Instruct-2506-Q3_K_S.gguf10.4 GB5fc9ae10dbb3cb273140a46b266f55f1df0fb07d1c6a2763f69e915a6238c338
Mistral-Small-3.2-24B-Instruct-2506-Q4_0.gguf13.5 GBc9057696280e025d7d018483873b1583b8d2efa1cf667995eca47d432852596a
Mistral-Small-3.2-24B-Instruct-2506-Q4_1.gguf14.9 GB2f532d1be65ada10eca6b2746fda6d17a3e55696a51138f0faddca5232c1efb7
Mistral-Small-3.2-24B-Instruct-2506-Q4_K_M.gguf14.3 GBa3cc56310807ed0d145eaf9f018ccda9ae7ad8edb41ec870aa2454b0d4700b3c
Mistral-Small-3.2-24B-Instruct-2506-Q4_K_S.gguf13.5 GB21451ba38ab682fc398575e4a86c890f8002ebf1d164dc32e9146c0802c6c9f9
Mistral-Small-3.2-24B-Instruct-2506-Q5_K_M.gguf16.8 GB423446013d1d9a63eb09d84f9d4fe0a1a81b57b6530260afd07a070b4e74845a
Mistral-Small-3.2-24B-Instruct-2506-Q5_K_S.gguf16.3 GB89bb4165bf99dd9d46148facc9876de98722f8910b14df174f4519a21af14aa4
Mistral-Small-3.2-24B-Instruct-2506-Q6_K.gguf19.3 GBe92f341791f7da41021d9ae7fac00a1c02aa007a09cb2ecabea748f73a206217
Mistral-Small-3.2-24B-Instruct-2506-Q8_0.gguf25.1 GBb2a40e5ef4eab8837d0462c303e8147ec754e2963e41916b551107d2b0ca6527
Mistral-Small-3.2-24B-Instruct-2506-UD-IQ1_M.gguf6.02 GB62b131e2d726938972ba768c5649a9dbaa7b5a39373ffc0f6178f8857c0b3f29
Mistral-Small-3.2-24B-Instruct-2506-UD-IQ1_S.gguf5.56 GB7fe31c9c23ebe7592d2a5c678c28a3b0a4434a0ec8e1f03efc40390d72ae6449
Mistral-Small-3.2-24B-Instruct-2506-UD-IQ2_M.gguf8.24 GB89c850cc7fcc7df59988204b402e377d1c3dfefc0137570b4a0795cd8e1de47c
Mistral-Small-3.2-24B-Instruct-2506-UD-IQ2_XXS.gguf6.75 GBb1bbe74d0b4c094c1b38bb6625415e4574f098119c10ba12c134a67574a17b2c
Mistral-Small-3.2-24B-Instruct-2506-UD-IQ3_XXS.gguf9.41 GBbbe9ab2e0090a73866422676d2b7fee43e5346c84dca50e265e2fa7c21379343
Mistral-Small-3.2-24B-Instruct-2506-UD-Q2_K_XL.gguf9.29 GB6d02f498d9f71b54a1286f1bed1797f844cded34cbbe6b8605ff5813bbcdcd89
Mistral-Small-3.2-24B-Instruct-2506-UD-Q3_K_XL.gguf11.9 GBee8f35a6dd72cd05d1c1ca741c180331f65d6718e0d41601bf5e911d1aea8b64
Mistral-Small-3.2-24B-Instruct-2506-UD-Q4_K_XL.gguf14.5 GBbcf82c1d4963f91d744d202efeee0724c0987625cd97a737ea2af68b49f141cf
Mistral-Small-3.2-24B-Instruct-2506-UD-Q5_K_XL.gguf16.8 GB37b9dac33314f5679870cefc1a5c7e8c0fd59b455a3355fff58ff13a87e33262
Mistral-Small-3.2-24B-Instruct-2506-UD-Q6_K_XL.gguf20.8 GBb5e7e1013443600be3c4eb9afd305e7156e5534efc7911050b2b8e4a680b1a2b
Mistral-Small-3.2-24B-Instruct-2506-UD-Q8_K_XL.gguf29.0 GB85312ebe3878946b621fc906a457d1a234a015d7fe83aebc0b9323f87d589b87
README.md26.9 KB5cf717c2eaa4a84dc3cbf20c254865d3f62198aaacec0b980d5613b0d10b17cf
imatrix_unsloth.gguf_file10.0 MB20e70abc28a6f6f1a4bc54b2452cfc44ee4521f28736f86b39d15fac6ff6e1b7
mmproj-BF16.gguf879 MBfd1cc76c8bdfc97ffa0af7429505173aed97edfa8ad5cbd605a72812b6eb2a29
mmproj-F16.gguf878 MBd6af684ae9136398eaa0b59ea9e0b0b850bb6ac5084f1e8c5cb8f85251825eaf
mmproj-F32.gguf1.76 GB02c8e1a274a44de00c97354d50539e14bf73256491ad5bf4812d1b5719bce09f
params131 B329302ef64629368e0115dc342740e3ba5f7475d0ebb4f511cb065d3a227babb
system2.33 KBac2123cf1c72bc66d9fad19597537a68b1144915a1d6b555db69e4eeab2ff973
template699 B706c4d1164f7f1bbce2a852f2bc9cfa66b98d24f79300b9034388e07fcda98c8
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
b750ec2299 pinned now2026-11-15 04:36 UTC35 files · 389 GB

Use it

$ fw get unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF
# manifest sha256 b192f1a00bc9… · 389 GB · 35 files
$ fw verify ./Mistral-Small-3.2-24B-Instruct-2506-GGUF unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF.json --dir ./Mistral-Small-3.2-24B-Instruct-2506-GGUF $ python3 fw.py verify ./Mistral-Small-3.2-24B-Instruct-2506-GGUF ./unsloth__Mistral-Small-3.2-24B-Instruct-2506-GGUF.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF --revision b750ec2299225e492f1bd27cab88a0a595fa848f --local-dir ./Mistral-Small-3.2-24B-Instruct-2506-GGUF
llama.cppDownload one GGUF variant (Mistral-Small-3.2-24B-Instruct-2506-UD-IQ1_S, 5.56 GB), then serve it.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF --include "Mistral-Small-3.2-24B-Instruct-2506-UD-IQ1_S*" --local-dir ./Mistral-Small-3.2-24B-Instruct-2506-GGUF $ llama-server -m ./Mistral-Small-3.2-24B-Instruct-2506-GGUF/Mistral-Small-3.2-24B-Instruct-2506-UD-IQ1_S.gguf
OllamaMake an Ollama model from the downloaded file.
$ printf 'FROM ./Mistral-Small-3.2-24B-Instruct-2506-GGUF/Mistral-Small-3.2-24B-Instruct-2506-UD-IQ1_S.gguf\n' > Modelfile $ ollama create mistral-small-3.2-24b-instruct-2506-gguf -f Modelfile

License

Name
Apache License 2.0
Id
Apache-2.0
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-15
Text in
README.md
Text sha256
5cf717c2eaa4a84dc3cbf20c254865d3f62198aaacec0b980d5613b0d10b17cf
Terms gate
No extra terms beyond the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: unsloth <huggingface.co/unsloth>Newsgroups: alt.freeweights.mistralSubject: Mistral-Small-3.2-24B-Instruct-2506-GGUFDate: 2026-11-15Commit: b750ec2Signed: 2DC28CC7B26A283B ✓
Upstream
unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF
Pinned commit
b750ec2299225e492f1bd27cab88a0a595fa848f
Manifest
Mistral-Small-3.2-24B-Instruct-2506-GGUF.json · .minisig
Manifest sha256
b192f1a00bc9cab0e44f643268bed6758ff2fc68e0bbb6e3d521b81bdc226948
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 04:36 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF.json $ curl -O https://freeweights.allternit.com/manifests/unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF.json.minisig $ minisign -Vm Mistral-Small-3.2-24B-Instruct-2506-GGUF.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

Parent

mistralai/Mistral-Small-3.2-24B-Instruct-2506 · this model is a quantization of it

No model in the catalog names this one as its parent.

Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF
Open in Freeweights Studio

Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest
minisign, key 2DC28CC7B26A283B. Mistral-Small-3.2-24B-Instruct-2506-GGUF.json. Provenance. Takedown history: none.
From: bartowski <huggingface.co/bartowski>Subject: alamios_Mistral-Small-3.1-DRAFT-0.5B-GGUF
From: microsoft <huggingface.co/microsoft>Subject: chatbench-mistral-7b
From: NousResearch <huggingface.co/NousResearch>Subject: DeepHermes-3-Mistral-24B-Preview
From: mistralai <huggingface.co/mistralai>Subject: Devstral-Small-2-24B-Instruct-2512
From: mlx-community <huggingface.co/mlx-community>Subject: Devstral-Small-2-24B-Instruct-2512-4bit
From: mlx-community <huggingface.co/mlx-community>Subject: Devstral-Small-2-24B-Instruct-2512-6bit