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---
base_model: mistralai/Pixtral-12B-2409
language:
- en
library_name: transformers
pipeline_tag: image-text-to-text
license: apache-2.0
tags:
- multimodal
- mistral
- pixtral
- unsloth
---
### ***Unsloth's [Dynamic 4-bit Quants](https://unsloth.ai/blog/dynamic-4bit) selectively avoids quantizing certain parameters, greatly improving accuracy while keeping VRAM usage similar to BnB 4-bit.<br>See our full collection of Unsloth quants on [Hugging Face here.](https://huggingface.co/collections/unsloth/unsloth-4-bit-dynamic-quants-67503bb873f89e15276c44e7)***
<br>
# Finetune Llama 3.2, Qwen 2.5, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tesla T4 notebook for Pixtral (12B) 2409 here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing
And a free notebook for [Llama 3.2 Vision (11B) here](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
# unsloth/Pixtral-12B-2409-unsloth-bnb-4bit
For more details on the model, please go to Mistral's original [model card](https://huggingface.co/mistralai/Pixtral-12B-2409)
## ✨ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Llama-3.2 (3B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 2.4x faster | 58% less |
| **Llama-3.2 (11B vision)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 2x faster | 40% less |
| **Qwen2 VL (7B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 1.8x faster | 40% less |
| **Qwen2.5 (7B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Kose-ucXO1IBaZq5BvbwWieuubP7hxvQ?usp=sharing) | 2x faster | 60% less |
| **Llama-3.1 (8B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 2.4x faster | 58% less |
| **Phi-3.5 (mini)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing) | 2x faster | 50% less |
| **Gemma 2 (9B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing) | 2.4x faster | 58% less |
| **Mistral (7B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less |
| **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less |
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/refs/heads/main/images/documentation%20green%20button.png" width="200"/>](https://docs.unsloth.ai)
- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
## Special Thanks
A huge thank you to the Mistral team for creating and releasing these models.
### Pixtral 2409 Details
Mistral common has image support! You can now pass images and URLs alongside text into the user message.
```
pip install --upgrade mistral_common
```
To use the model checkpoint:
```
# pip install huggingface-hub
from huggingface_hub import snapshot_download
snapshot_download(repo_id="mistral-community/pixtral-12b-240910", local_dir="...")
```
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╓────────────────────────────────────────────────────────────────────────────╖
║ PIXTRAL - 12B - v0.1 10/09/24 ║
╙────────────────────────────────────────────────────────────────────────────╜
╓────────────────────────────────────────────────────────────────────────────╖
║ ║
║ ·· md5sum ·· ║
║ ║
║ b8e9126ef0c15a1130c14b15e8432a67 consolidated.safetensors ║
║ 68b39355a7b14a7d653292dab340a0be params.json ║
║ 10229adc84036ff8fe44a2a8e2ad9ba9 tekken.json ║
╙────────────────────────────────────────────────────────────────────────────╜
╓────────────────────────────────────────────────────────────────────────────╖
║ ║
║ ·· Released by the Mistral AI team ·· ║
║ ║
║ - Use GELU for the vision adapter ║
║ - Use 2D ROPE for the vision encoder ║
║ ║
╙────────────────────────────────────────────────────────────────────────────╜
## Images
You can encode images as follows
```python
from mistral_common.protocol.instruct.messages import (
UserMessage,
TextChunk,
ImageURLChunk,
ImageChunk,
)
from PIL import Image
from mistral_common.protocol.instruct.request import ChatCompletionRequest
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
tokenizer = MistralTokenizer.from_model("pixtral")
image = Image.new('RGB', (64, 64))
# tokenize images and text
tokenized = tokenizer.encode_chat_completion(
ChatCompletionRequest(
messages=[
UserMessage(
content=[
TextChunk(text="Describe this image"),
ImageChunk(image=image),
]
)
],
model="pixtral",
)
)
tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images
# Count the number of tokens
print("# tokens", len(tokens))
print("# images", len(images))
```
## Image URLs
You can pass image url which will be automatically downloaded
```python
url_dog = "https://picsum.photos/id/237/200/300"
url_mountain = "https://picsum.photos/seed/picsum/200/300"
# tokenize image urls and text
tokenized = tokenizer.encode_chat_completion(
ChatCompletionRequest(
messages=[
UserMessage(
content=[
TextChunk(text="Can this animal"),
ImageURLChunk(image_url=url_dog),
TextChunk(text="live here?"),
ImageURLChunk(image_url=url_mountain),
]
)
],
model="pixtral",
)
)
tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images
# Count the number of tokens
print("# tokens", len(tokens))
print("# images", len(images))
```
# ImageData
You can also pass image encoded as base64
```python
tokenized = tokenizer.encode_chat_completion(
ChatCompletionRequest(
messages=[
UserMessage(
content=[
TextChunk(text="What is this?"),
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"),
]
)
],
model="pixtral",
)
)
tokens, text, images = tokenized.tokens, tokenized.text, tokenized.images
# Count the number of tokens
print("# tokens", len(tokens))
print("# images", len(images))
```
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