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llmfan46/Mistral-Small-3.2-24B-Instruct-2506-ultra-uncensored-heretic

llmfan46 Mistral 24B multimodal
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Response includes
  • classification m3
  • files 14
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Abliteration classifier · v1.0.0
M3
Primary method

Layer-wise ablation

Applied on top of direct removal inherited from the base model.
Confidence
HIGH
Inherited from base model
Why this label 2 signals
Producer identity confirmed by naming conventions, tags or the model card. This label is very unlikely to change.
  • 'heretic' in model name (Heretic-produced)
  • Heretic uses layer-wise optimization (M3) with underlying direction removal (M1)
Refusal direction extracted via
Extraction technique

Difference-of-means

Confidence
HIGH
Why we say so
name contains 'heretic'; Heretic default extraction is difference-of-means (Arditi 2024)
Downloads · lifetime
2K
222 last 30d - stable
Likes
9
Descendants
4
in 4 direct forks
Model age
6mo ago
created 2026-03-19
Available via
1 provider
featherless-ai
Downloads over time
Now1.8K→from226↑692%
1487471.3K1.9K226 on Mar 181.8K on Oct 11MarAprMayJunJulAugSepOct
Mar 18 → Oct 11 · 69 snapshots · spans 207 days

Benchmarks

Benchmark Score Source
Entertainment 0.6 UGI
Hazardous 0.6 UGI
Natural Intelligence 5.09 UGI
Political lean NA UGI
Sensitive-Info 4.06 UGI
SocPol 0 UGI
UGI 4.38 UGI
Willingness (10) 0.5 UGI
W10-Adherence 0 UGI
W10-Direct 1 UGI
Writing NA UGI

Genealogy 4 direct forks

Full fork graph →

This model's place in the market. Above: what it was derived from. Below: the tree of everything derived from it.

Variants by this author 2 formats · 6K downloads combined

The same weights this author released in different packaging. Pick the format that matches your runtime.

Metadata

License
apache-2.0
Languages
en fr de es pt it ja ko ru zh ar fa id ms ne pl ro sr sv tr uk vi hi bn
Tags
vllm safetensors mistral3 heretic uncensored decensored abliterated ara image-text-to-text conversational en fr

Related

Total size
44.7 GB
Files
14
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-03-27 22:32

Files by quantization

Auxiliary files 14 files 44.8 GB
model.safetensors 44.7 GB d94ad8fc download
tekken.json 18.5 MB 6e250168 download
tokenizer.json 16.3 MB c46ef3c5 download
README.md 27.9 KB b183697d download
special_tokens_map.json 20.9 KB 85ee48c5 download
chat_template.jinja 14.8 KB 6b999bcd download
SYSTEM_PROMPT.txt 2.33 KB b3b26d71 download
config.json 1.59 KB b12cf308 download
.gitattributes 1.58 KB c684f12c download
params.json 768 B 94420c6d download
preprocessor_config.json 662 B 2f6e7aee download
tokenizer_config.json 423 B b8142b69 download
processor_config.json 189 B 0092b0f4 download
generation_config.json 179 B c7f80867 download

README current version from Hugging Face


language:

  • en
  • fr
  • de
  • es
  • pt
  • it
  • ja
  • ko
  • ru
  • zh
  • ar
  • fa
  • id
  • ms
  • ne
  • pl
  • ro
  • sr
  • sv
  • tr
  • uk
  • vi
  • hi
  • bn
    license: apache-2.0
    library_name: vllm
    inference: false
    base_model:
  • mistralai/Mistral-Small-3.2-24B-Instruct-2506
    pipeline_tag: image-text-to-text
    tags:
  • heretic
  • uncensored
  • decensored
  • abliterated
  • ara

🚨⚠️ I HAVE REACHED HUGGING FACE'S FREE STORAGE LIMIT ⚠️🚨

I can no longer upload new models unless I can cover the cost of additional storage.
I host 70+ free models as an independent contributor and this work is unpaid.
Without your support, no more new models can be uploaded.

🎉 Patreon (Monthly)  |  ☕ Ko-fi (One-time)

Every contribution goes directly toward Hugging Face storage fees to keep models free for everyone.


98% fewer refusals (2/100 Uncensored vs 98/100 Original) while preserving model quality (0.0369 KL divergence).

❤️ Support My Work

Creating these models takes significant time, work and compute. If you find them useful consider supporting me:

Platform Link What you get
🎉 Patreon Monthly support Priority model requests
☕ Ko-fi One-time tip My eternal gratitude

Your help will motivate me and would go into further improving my workflow and coverings fees for storage, compute and may even help uncensoring bigger model with rental Cloud GPUs.


This is a decensored version of mistralai/Mistral-Small-3.2-24B-Instruct-2506, made using Heretic v1.2.0 with the Arbitrary-Rank Ablation (ARA) method

Abliteration parameters

Parameter Value
start_layer_index 7
end_layer_index 35
preserve_good_behavior_weight 0.7891
steer_bad_behavior_weight 0.0002
overcorrect_relative_weight 1.0413
neighbor_count 6

Targeted components

attn.o_proj

Performance

Metric This model Original model (Mistral-Small-3.2-24B-Instruct-2506)
KL divergence 0.0369 0 (by definition)
Refusals ✅ 2/100 ❌ 98/100

Lower refusals indicate fewer content restrictions, while lower KL divergence indicates better preservation of the original model's capabilities. Higher refusals cause more rejections, objections, pushbacks, lecturing, censorship, softening and deflections, while higher KL divergence degrades coherence, reasoning ability, and overall quality.

GGUF Version

GGUF quantizations available here llmfan46/Mistral-Small-3.2-24B-Instruct-2506-ultra-uncensored-heretic-GGUF.


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

Model Wildbench v2 Arena Hard v2 IF (Internal; accuracy)
Small 3.1 24B Instruct 55.6% 19.56% 82.75%
Small 3.2 24B Instruct 65.33% 43.1% 84.78%

Infinite Generations

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

Model Infinite Generations (Internal; Lower is better)
Small 3.1 24B Instruct 2.11%
Small 3.2 24B Instruct 1.29%

STEM

Model MMLU MMLU Pro (5-shot CoT) MATH GPQA Main (5-shot CoT) GPQA Diamond (5-shot CoT ) MBPP Plus - Pass@5 HumanEval Plus - Pass@5 SimpleQA (TotalAcc)
Small 3.1 24B Instruct 80.62% 66.76% 69.30% 44.42% 45.96% 74.63% 88.99% 10.43%
Small 3.2 24B Instruct 80.50% 69.06% 69.42% 44.22% 46.13% 78.33% 92.90% 12.10%

Vision

Model MMMU Mathvista ChartQA DocVQA AI2D
Small 3.1 24B Instruct 64.00% 68.91% 86.24% 94.08% 93.72%
Small 3.2 24B Instruct 62.50% 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 tailor it to 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.

vLLM (recommended)

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 recommend 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

Leverage the vision capabilities of Mistral-Small-3.2-24B-Instruct-2506 to make 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.

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