Mistral-Small-3.1-24B-Instruct-2503 GGUF Models

Model Generation Details

This model was generated using llama.cpp at commit 92ecdcc0.

Ultra-Low-Bit Quantization with IQ-DynamicGate (1-2 bit)

Our latest quantization method introduces precision-adaptive quantization for ultra-low-bit models (1-2 bit), with benchmark-proven improvements on Llama-3-8B. This approach uses layer-specific strategies to preserve accuracy while maintaining extreme memory efficiency.

Benchmark Context

All tests conducted on Llama-3-8B-Instruct using:

Standard perplexity evaluation pipeline
2048-token context window
Same prompt set across all quantizations

Method

Dynamic Precision Allocation:
- First/Last 25% of layers → IQ4_XS (selected layers)
- Middle 50% → IQ2_XXS/IQ3_S (increase efficiency)
Critical Component Protection:
- Embeddings/output layers use Q5_K
- Reduces error propagation by 38% vs standard 1-2bit

Quantization Performance Comparison (Llama-3-8B)

Quantization	Standard PPL	DynamicGate PPL	Δ PPL	Std Size	DG Size	Δ Size	Std Speed	DG Speed
IQ2_XXS	11.30	9.84	-12.9%	2.5G	2.6G	+0.1G	234s	246s
IQ2_XS	11.72	11.63	-0.8%	2.7G	2.8G	+0.1G	242s	246s
IQ2_S	14.31	9.02	-36.9%	2.7G	2.9G	+0.2G	238s	244s
IQ1_M	27.46	15.41	-43.9%	2.2G	2.5G	+0.3G	206s	212s
IQ1_S	53.07	32.00	-39.7%	2.1G	2.4G	+0.3G	184s	209s

Key:

PPL = Perplexity (lower is better)
Δ PPL = Percentage change from standard to DynamicGate
Speed = Inference time (CPU avx2, 2048 token context)
Size differences reflect mixed quantization overhead

Key Improvements:

🔥 IQ1_M shows massive 43.9% perplexity reduction (27.46 → 15.41)
🚀 IQ2_S cuts perplexity by 36.9% while adding only 0.2GB
⚡ IQ1_S maintains 39.7% better accuracy despite 1-bit quantization

Tradeoffs:

All variants have modest size increases (0.1-0.3GB)
Inference speeds remain comparable (<5% difference)

When to Use These Models

📌 Fitting models into GPU VRAM

✔ Memory-constrained deployments

✔ Cpu and Edge Devices where 1-2bit errors can be tolerated

✔ Research into ultra-low-bit quantization

Choosing the Right Model Format

Selecting the correct model format depends on your hardware capabilities and memory constraints.

BF16 (Brain Float 16) – Use if BF16 acceleration is available

A 16-bit floating-point format designed for faster computation while retaining good precision.
Provides similar dynamic range as FP32 but with lower memory usage.
Recommended if your hardware supports BF16 acceleration (check your device's specs).
Ideal for high-performance inference with reduced memory footprint compared to FP32.

📌 Use BF16 if:
✔ Your hardware has native BF16 support (e.g., newer GPUs, TPUs).
✔ You want higher precision while saving memory.
✔ You plan to requantize the model into another format.

📌 Avoid BF16 if:
❌ Your hardware does not support BF16 (it may fall back to FP32 and run slower).
❌ You need compatibility with older devices that lack BF16 optimization.

F16 (Float 16) – More widely supported than BF16

A 16-bit floating-point high precision but with less of range of values than BF16.
Works on most devices with FP16 acceleration support (including many GPUs and some CPUs).
Slightly lower numerical precision than BF16 but generally sufficient for inference.

📌 Use F16 if:
✔ Your hardware supports FP16 but not BF16.
✔ You need a balance between speed, memory usage, and accuracy.
✔ You are running on a GPU or another device optimized for FP16 computations.

📌 Avoid F16 if:
❌ Your device lacks native FP16 support (it may run slower than expected).
❌ You have memory limitations.

Quantized Models (Q4_K, Q6_K, Q8, etc.) – For CPU & Low-VRAM Inference

Quantization reduces model size and memory usage while maintaining as much accuracy as possible.

Lower-bit models (Q4_K) → Best for minimal memory usage, may have lower precision.
Higher-bit models (Q6_K, Q8_0) → Better accuracy, requires more memory.

📌 Use Quantized Models if:
✔ You are running inference on a CPU and need an optimized model.
✔ Your device has low VRAM and cannot load full-precision models.
✔ You want to reduce memory footprint while keeping reasonable accuracy.

📌 Avoid Quantized Models if:
❌ You need maximum accuracy (full-precision models are better for this).
❌ Your hardware has enough VRAM for higher-precision formats (BF16/F16).

Very Low-Bit Quantization (IQ3_XS, IQ3_S, IQ3_M, Q4_K, Q4_0)

These models are optimized for extreme memory efficiency, making them ideal for low-power devices or large-scale deployments where memory is a critical constraint.

IQ3_XS: Ultra-low-bit quantization (3-bit) with extreme memory efficiency.
- Use case: Best for ultra-low-memory devices where even Q4_K is too large.
- Trade-off: Lower accuracy compared to higher-bit quantizations.
IQ3_S: Small block size for maximum memory efficiency.
- Use case: Best for low-memory devices where IQ3_XS is too aggressive.
IQ3_M: Medium block size for better accuracy than IQ3_S.
- Use case: Suitable for low-memory devices where IQ3_S is too limiting.
Q4_K: 4-bit quantization with block-wise optimization for better accuracy.
- Use case: Best for low-memory devices where Q6_K is too large.
Q4_0: Pure 4-bit quantization, optimized for ARM devices.
- Use case: Best for ARM-based devices or low-memory environments.

Summary Table: Model Format Selection

Model Format	Precision	Memory Usage	Device Requirements	Best Use Case
BF16	Highest	High	BF16-supported GPU/CPUs	High-speed inference with reduced memory
F16	High	High	FP16-supported devices	GPU inference when BF16 isn't available
Q4_K	Medium Low	Low	CPU or Low-VRAM devices	Best for memory-constrained environments
Q6_K	Medium	Moderate	CPU with more memory	Better accuracy while still being quantized
Q8_0	High	Moderate	CPU or GPU with enough VRAM	Best accuracy among quantized models
IQ3_XS	Very Low	Very Low	Ultra-low-memory devices	Extreme memory efficiency and low accuracy
Q4_0	Low	Low	ARM or low-memory devices	llama.cpp can optimize for ARM devices

Included Files & Details

`Mistral-Small-3.1-24B-Instruct-2503-bf16.gguf`

Model weights preserved in BF16.
Use this if you want to requantize the model into a different format.
Best if your device supports BF16 acceleration.

`Mistral-Small-3.1-24B-Instruct-2503-f16.gguf`

Model weights stored in F16.
Use if your device supports FP16, especially if BF16 is not available.

`Mistral-Small-3.1-24B-Instruct-2503-bf16-q8_0.gguf`

Output & embeddings remain in BF16.
All other layers quantized to Q8_0.
Use if your device supports BF16 and you want a quantized version.

`Mistral-Small-3.1-24B-Instruct-2503-f16-q8_0.gguf`

Output & embeddings remain in F16.
All other layers quantized to Q8_0.

`Mistral-Small-3.1-24B-Instruct-2503-q4_k.gguf`

Output & embeddings quantized to Q8_0.
All other layers quantized to Q4_K.
Good for CPU inference with limited memory.

`Mistral-Small-3.1-24B-Instruct-2503-q4_k_s.gguf`

Smallest Q4_K variant, using less memory at the cost of accuracy.
Best for very low-memory setups.

`Mistral-Small-3.1-24B-Instruct-2503-q6_k.gguf`

Output & embeddings quantized to Q8_0.
All other layers quantized to Q6_K .

`Mistral-Small-3.1-24B-Instruct-2503-q8_0.gguf`

Fully Q8 quantized model for better accuracy.
Requires more memory but offers higher precision.

`Mistral-Small-3.1-24B-Instruct-2503-iq3_xs.gguf`

IQ3_XS quantization, optimized for extreme memory efficiency.
Best for ultra-low-memory devices.

`Mistral-Small-3.1-24B-Instruct-2503-iq3_m.gguf`

IQ3_M quantization, offering a medium block size for better accuracy.
Suitable for low-memory devices.

`Mistral-Small-3.1-24B-Instruct-2503-q4_0.gguf`

Pure Q4_0 quantization, optimized for ARM devices.
Best for low-memory environments.
Prefer IQ4_NL for better accuracy.

🚀 If you find these models useful

❤ Please click "Like" if you find this useful!
Help me test my AI-Powered Network Monitor Assistant with quantum-ready security checks:
👉 Free Network Monitor

💬 How to test:
Choose an AI assistant type:

TurboLLM (GPT-4o-mini)
HugLLM (Hugginface Open-source)
TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

Function calling against live network services
How small can a model go while still handling:
- Automated Nmap scans
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🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads):

✅ Zero-configuration setup
⏳ 30s load time (slow inference but no API costs)
🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4o-mini for:

Create custom cmd processors to run .net code on Free Network Monitor Agents
Real-time network diagnostics and monitoring
Security Audits
Penetration testing (Nmap/Metasploit)
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🌐 Runs on Hugging Face Inference API

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"Run a comprehensive security audit on my server"
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Model Card for Mistral-Small-3.1-24B-Instruct-2503

Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks.
This model is an instruction-finetuned version of: Mistral-Small-3.1-24B-Base-2503.

Mistral Small 3.1 can be deployed locally and is exceptionally "knowledge-dense," fitting within a single RTX 4090 or a 32GB RAM MacBook once quantized.

It is ideal for:

Fast-response conversational agents.
Low-latency function calling.
Subject matter experts via fine-tuning.
Local inference for hobbyists and organizations handling sensitive data.
Programming and math reasoning.
Long document understanding.
Visual understanding.

For enterprises requiring specialized capabilities (increased context, specific modalities, domain-specific knowledge, etc.), we will release commercial models beyond what Mistral AI contributes to the community.

Learn more about Mistral Small 3.1 in our blog post.

Key Features

Vision: Vision capabilities enable the model to analyze images and provide insights based on visual content in addition to text.
Multilingual: Supports dozens of languages, including English, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Malay, Nepali, Polish, Portuguese, Romanian, Russian, Serbian, Spanish, Swedish, Turkish, Ukrainian, Vietnamese, Arabic, Bengali, Chinese, Farsi.
Agent-Centric: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
Advanced Reasoning: State-of-the-art conversational and reasoning capabilities.
Apache 2.0 License: Open license allowing usage and modification for both commercial and non-commercial purposes.
Context Window: A 128k context window.
System Prompt: Maintains strong adherence and support for system prompts.
Tokenizer: Utilizes a Tekken tokenizer with a 131k vocabulary size.

Benchmark Results

When available, we report numbers previously published by other model providers, otherwise we re-evaluate them using our own evaluation harness.

Pretrain Evals

Model	MMLU (5-shot)	MMLU Pro (5-shot CoT)	TriviaQA	GPQA Main (5-shot CoT)	MMMU
Small 3.1 24B Base	81.01%	56.03%	80.50%	37.50%	59.27%
Gemma 3 27B PT	78.60%	52.20%	81.30%	24.30%	56.10%

Instruction Evals

Text

Model	MMLU	MMLU Pro (5-shot CoT)	MATH	GPQA Main (5-shot CoT)	GPQA Diamond (5-shot CoT )	MBPP	HumanEval	SimpleQA (TotalAcc)
Small 3.1 24B Instruct	80.62%	66.76%	69.30%	44.42%	45.96%	74.71%	88.41%	10.43%
Gemma 3 27B IT	76.90%	67.50%	89.00%	36.83%	42.40%	74.40%	87.80%	10.00%
GPT4o Mini	82.00%	61.70%	70.20%	40.20%	39.39%	84.82%	87.20%	9.50%
Claude 3.5 Haiku	77.60%	65.00%	69.20%	37.05%	41.60%	85.60%	88.10%	8.02%
Cohere Aya-Vision 32B	72.14%	47.16%	41.98%	34.38%	33.84%	70.43%	62.20%	7.65%

Vision

Model	MMMU	MMMU PRO	Mathvista	ChartQA	DocVQA	AI2D	MM MT Bench
Small 3.1 24B Instruct	64.00%	49.25%	68.91%	86.24%	94.08%	93.72%	7.3
Gemma 3 27B IT	64.90%	48.38%	67.60%	76.00%	86.60%	84.50%	7
GPT4o Mini	59.40%	37.60%	56.70%	76.80%	86.70%	88.10%	6.6
Claude 3.5 Haiku	60.50%	45.03%	61.60%	87.20%	90.00%	92.10%	6.5
Cohere Aya-Vision 32B	48.20%	31.50%	50.10%	63.04%	72.40%	82.57%	4.1

Multilingual Evals

Model	Average	European	East Asian	Middle Eastern
Small 3.1 24B Instruct	71.18%	75.30%	69.17%	69.08%
Gemma 3 27B IT	70.19%	74.14%	65.65%	70.76%
GPT4o Mini	70.36%	74.21%	65.96%	70.90%
Claude 3.5 Haiku	70.16%	73.45%	67.05%	70.00%
Cohere Aya-Vision 32B	62.15%	64.70%	57.61%	64.12%

Long Context Evals

Model	LongBench v2	RULER 32K	RULER 128K
Small 3.1 24B Instruct	37.18%	93.96%	81.20%
Gemma 3 27B IT	34.59%	91.10%	66.00%
GPT4o Mini	29.30%	90.20%	65.8%
Claude 3.5 Haiku	35.19%	92.60%	91.90%

Basic Instruct Template (V7-Tekken)

<s>[SYSTEM_PROMPT]<system prompt>[/SYSTEM_PROMPT][INST]<user message>[/INST]<assistant response></s>[INST]<user message>[/INST]

<system_prompt>, <user message> and <assistant response> are placeholders.

Please make sure to use mistral-common as the source of truth

Usage

The model can be used with the following frameworks;

vllm (recommended): See here

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 the following system prompt:

system_prompt = """You are Mistral Small 3.1, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.
You power an AI assistant called Le Chat.
Your knowledge base was last updated on 2023-10-01.
The current date is {today}.

When you're not sure about some information, you say that you don't have the information and don't make up anything.
If the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. "What are some good restaurants around me?" => "Where are you?" or "When is the next flight to Tokyo" => "Where do you travel from?").
You are always very attentive to dates, in particular you try to resolve dates (e.g. "yesterday" is {yesterday}) and when asked about information at specific dates, you discard information that is at another date.
You follow these instructions in all languages, and always respond to the user in the language they use or request.
Next sections describe the capabilities that you have.

# WEB BROWSING INSTRUCTIONS

You cannot perform any web search or access internet to open URLs, links etc. If it seems like the user is expecting you to do so, you clarify the situation and ask the user to copy paste the text directly in the chat.

# MULTI-MODAL INSTRUCTIONS

You have the ability to read images, but you cannot generate images. You also cannot transcribe audio files or videos.
You cannot read nor transcribe audio files or videos."""

vLLM (recommended)

We recommend using this model with the vLLM library to implement production-ready inference pipelines.

Installation

Make sure you install vLLM >= 0.8.1:

pip install vllm --upgrade

Doing so should automatically install mistral_common >= 1.5.4.

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.

Server

We recommand that you use Mistral-Small-3.1-24B-Instruct-2503 in a server/client setting.

Spin up a server:

vllm serve mistralai/Mistral-Small-3.1-24B-Instruct-2503 --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-3.1-24B-Instruct-2503 on GPU requires ~55 GB of GPU RAM in bf16 or fp16.

To ping the client you can use a simple Python snippet.

import requests
import json
from huggingface_hub import hf_hub_download
from datetime import datetime, timedelta

url = "http://<your-server-url>:8000/v1/chat/completions"
headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}

model = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"


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)


SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")

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

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Which of the depicted countries has the best food? Which the second and third and fourth? Name the country, its color on the map and one its city that is visible on the map, but is not the capital. Make absolutely sure to only name a city that can be seen on the map.",
            },
            {"type": "image_url", "image_url": {"url": image_url}},
        ],
    },
]

data = {"model": model, "messages": messages, "temperature": 0.15}

response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json()["choices"][0]["message"]["content"])
# Determining the "best" food is highly subjective and depends on personal preferences. However, based on general popularity and recognition, here are some countries known for their cuisine:

# 1. **Italy** - Color: Light Green - City: Milan
#    - Italian cuisine is renowned worldwide for its pasta, pizza, and various regional specialties.

# 2. **France** - Color: Brown - City: Lyon
#    - French cuisine is celebrated for its sophistication, including dishes like coq au vin, bouillabaisse, and pastries like croissants and éclairs.

# 3. **Spain** - Color: Yellow - City: Bilbao
#    - Spanish cuisine offers a variety of flavors, from paella and tapas to jamón ibérico and churros.

# 4. **Greece** - Not visible on the map
#    - Greek cuisine is known for dishes like moussaka, souvlaki, and baklava. Unfortunately, Greece is not visible on the provided map, so I cannot name a city.

# Since Greece is not visible on the map, I'll replace it with another country known for its good food:

# 4. **Turkey** - Color: Light Green (east part of the map) - City: Istanbul
#    - Turkish cuisine is diverse and includes dishes like kebabs, meze, and baklava.

Function calling

Mistral-Small-3.1-24-Instruct-2503 is excellent at function / tool calling tasks via vLLM. E.g.:

Example

import requests
import json
from huggingface_hub import hf_hub_download
from datetime import datetime, timedelta

url = "http://<your-url>:8000/v1/chat/completions"
headers = {"Content-Type": "application/json", "Authorization": "Bearer token"}

model = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"


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)


SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")


tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "The city to find the weather for, e.g. 'San Francisco'",
                    },
                    "state": {
                        "type": "string",
                        "description": "The state abbreviation, e.g. 'CA' for California",
                    },
                    "unit": {
                        "type": "string",
                        "description": "The unit for temperature",
                        "enum": ["celsius", "fahrenheit"],
                    },
                },
                "required": ["city", "state", "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": "Can you tell me what the temperature will be in Dallas, in Fahrenheit?",
    },
]

data = {"model": model, "messages": messages, "tools": tools, "temperature": 0.15}

response = requests.post(url, headers=headers, data=json.dumps(data))
print(response.json()["choices"][0]["message"]["tool_calls"])
# [{'id': '8PdihwL6d', 'type': 'function', 'function': {'name': 'get_current_weather', 'arguments': '{"city": "Dallas", "state": "TX", "unit": "fahrenheit"}'}}]

Offline

from vllm import LLM
from vllm.sampling_params import SamplingParams
from datetime import datetime, timedelta

SYSTEM_PROMPT = "You are a conversational agent that always answers straight to the point, always end your accurate response with an ASCII drawing of a cat."

user_prompt = "Give me 5 non-formal ways to say 'See you later' in French."

messages = [
    {
        "role": "system",
        "content": SYSTEM_PROMPT
    },
    {
        "role": "user",
        "content": user_prompt
    },
]
model_name = "mistralai/Mistral-Small-3.1-24B-Instruct-2503"
# note that running this model on GPU requires over 60 GB of GPU RAM
llm = LLM(model=model_name, tokenizer_mode="mistral")

sampling_params = SamplingParams(max_tokens=512, temperature=0.15)
outputs = llm.chat(messages, sampling_params=sampling_params)

print(outputs[0].outputs[0].text)
# Here are five non-formal ways to say "See you later" in French:

# 1. **À plus tard** - Until later
# 2. **À toute** - See you soon (informal)
# 3. **Salut** - Bye (can also mean hi)
# 4. **À plus** - See you later (informal)
# 5. **Ciao** - Bye (informal, borrowed from Italian)

# ```
#  /\_/\
# ( o.o )
#  > ^ <
# ```

Transformers (untested)

Transformers-compatible model weights are also uploaded (thanks a lot @cyrilvallez). However the transformers implementation was not throughly tested, but only on "vibe-checks". Hence, we can only ensure 100% correct behavior when using the original weight format with vllm (see above).

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GGUF

Model size

23.6B params

Architecture

llama

Hardware compatibility

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Model tree for Mungert/Mistral-Small-3.1-24B-Instruct-2503-GGUF

Base model

mistralai/Mistral-Small-3.1-24B-Base-2503

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