alt.freeweights.mistral / Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit
From: unsloth <huggingface.co/unsloth>Newsgroups: alt.freeweights.mistralSubject:

Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit

Date: 2026-11-15Commit: 0773558Signed: 2DC28CC7B26A283B ✓

Image text to text model with 24.7B parameters. Safetensors weights, 14.5 GB in all. Published by unsloth under Apache-2.0.

24.7B params14.5GBMistral-Small-3.2-24B-Instruct-2506-bnb-4bitsafetensorspermissive
FactsParams24.7BSize14.5 GBFormatssafetensorsTaskimage text to textLicenseApache-2.0From sourcepermissive
$ fw get unsloth/Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit
# manifest sha256 a8dee0665c5d… · 14.5 GB · 14 files
$ fw verify ./Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit unsloth/Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit
From sourceHosted (signed)unsloth/Mistral-Small-3.2-24B-Instruct-2506-bnb-4bitLast check: 2026-11-15 04:54 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

Overview

Size
14.5 GB
Weights
14.4 GB (safetensors)
Parameters
24.7B
Formats
safetensors
Task
image text to text
License
Apache-2.0 (permissive)
Files
14, 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 0773558. Shown as plain text with links; images open at the publisher.

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

14 files · 14.5 GBWeights 14.4 GB (safetensors). Sizes and sha256 from the signed manifest.
Hide all 14 files with their sha256
FileSizesha256
.gitattributes1.57 KB34448b82c17d60fec9b65b1f093c115ddbaadc04beb1b0140b6bfed2e012a930
README.md25.3 KBea0c21b918325fa4e82d2f81839bb30384b459041db446366374559631f6ddf6
chat_template.jinja14.7 KBcafc16d39f41333a645464fea4b5f6bef86924c541e3e96071bcb7ebf107a6e8
config.json1.90 KB82148f329c20dbc07357ad17d99cb63532089c4149b25b5c2787f7fcb1952235
generation_config.json171 B90c98e4ee5bcd481c9429b6cd08ac8241c89ccb1d8e352b32292e41f9739e543
model-00001-of-00003.safetensors4.98 GB8c67b42ee6f7e7c524d95043cd0838bb8bdffb0957f4f7c659a87e4bc5a91942
model-00002-of-00003.safetensors4.99 GB39bbc995b15b6f35f317e6b203ec72cabd79a62fa9123467653f96a36ef8068d
model-00003-of-00003.safetensors4.47 GBbfee5ca26a8ce39ccbd53227afbf6c4cb64f3358d9ee3b1512ac6f4f4364569b
model.safetensors.index.json315 KBb7912a82d6d3755cd254c1cf273a949367171dd236d7bab535175e983c12c388
preprocessor_config.json634 B0f1a312ed75c86bccdf65333b5b43507f597028d895dbb9cf9f16be9f87b52f1
processor_config.json189 B4ceef0d0cbf062ffd522d85fcbf5248c2db8659c589afcc47b2f26810e7d9a58
special_tokens_map.json21.4 KBeea95589f0339b15d91e77ae59d303331d81b46b9006a32f857a078312146ac0
tokenizer.json17.1 MB23ad081f384b2bdb3c97f6e5461dfce52c1174c7328854a55006988f0fef9da7
tokenizer_config.json213 KB03c113fd1b87abc79b0ac4fa6fb4f366f425a45efe4b1a342bdd77ee63a045b5
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
07735581e6 pinned now2026-11-15 04:54 UTC14 files · 14.5 GB

Use it

$ fw get unsloth/Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit
# manifest sha256 a8dee0665c5d… · 14.5 GB · 14 files
$ fw verify ./Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit unsloth/Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit
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-bnb-4bit.json --dir ./Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit $ python3 fw.py verify ./Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit ./unsloth__Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit.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-bnb-4bit --revision 07735581e6480b532be5e8433813df15108cb66a --local-dir ./Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit
transformersLoad the weights in Python.
import os os.environ["HF_ENDPOINT"] = "https://freeweights.allternit.com" from transformers import AutoModelForImageTextToText, AutoProcessor processor = AutoProcessor.from_pretrained("unsloth/Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit", revision="07735581e6480b532be5e8433813df15108cb66a") model = AutoModelForImageTextToText.from_pretrained("unsloth/Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit", revision="07735581e6480b532be5e8433813df15108cb66a", device_map="auto")
vLLMServe an OpenAI-compatible API.
$ HF_ENDPOINT=https://freeweights.allternit.com vllm serve unsloth/Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit --revision 07735581e6480b532be5e8433813df15108cb66a

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
ea0c21b918325fa4e82d2f81839bb30384b459041db446366374559631f6ddf6
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-bnb-4bitDate: 2026-11-15Commit: 0773558Signed: 2DC28CC7B26A283B ✓
Upstream
unsloth/Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit
Pinned commit
07735581e6480b532be5e8433813df15108cb66a
Manifest
Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit.json · .minisig
Manifest sha256
a8dee0665c5d1b6947a60f1e9a967d1370ec5ba258d424ff0996055aa4cec28a
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 04:54 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-bnb-4bit.json $ curl -O https://freeweights.allternit.com/manifests/unsloth/Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit.json.minisig $ minisign -Vm Mistral-Small-3.2-24B-Instruct-2506-bnb-4bit.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-bnb-4bit
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-bnb-4bit.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