---
title: Pixtral-12B-2409
canonical_url: "https://www.modelscope.cn/models/LLM-Research/Pixtral-12B-2409"
md_url: "https://www.modelscope.cn/models/LLM-Research/Pixtral-12B-2409.md"
repository: LLM-Research/Pixtral-12B-2409
last_updated: 2024-09-12
license: apache-2.0
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
parameters: 12.7B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - Pytorch
downloads: 87514
stars: 0
---

# Pixtral-12B-2409

> Pixtral-12B-2409 - LLM-Research 在 ModelScope 开源的模型。[!WARNING] We still need to validate official evaluations with the below usage example.

LLM-Research/Pixtral-12B-2409 是 ModelScope 魔搭社区上的 12.7B 参数image-text-to-text模型，采用 apache-2.0 许可。

- **Repository**: LLM-Research/Pixtral-12B-2409
- **License**: apache-2.0
- **Tasks**: image-text-to-text
- **Parameters**: 12.7B
- **Downloads**: 87514
- **Stars**: 0
- **Last updated**: 2024-09-12

Source: https://www.modelscope.cn/models/LLM-Research/Pixtral-12B-2409

---

# Pixtral-12B-0910

> [!WARNING]
> We still need to validate official evaluations with the below usage example.


...TODO

## Usage

We recommend using Pixtral with the [vLLM library](https://github.com/vllm-project/vllm).

**Important**: Make sure you have installed vLLM from source - more specifically make sure you have installed [this commit (TODO)]( ).
Also make sure you have `mistral_common >= 1.4.0` installed:

```
pip install --upgrade mistral_common
```

**_Simple Example_**

```py
from vllm import LLM
from vllm.sampling_params import SamplingParams

model_name = "mistralai/Pixtral-12B-2409"

sampling_params = SamplingParams(max_tokens=8192)

llm = LLM(model=model_name, tokenizer_mode="mistral")

prompt = "Describe this image in one sentence."
image_url = "https://picsum.photos/id/237/200/300"

messages = [
    {
        "role": "user",
        "content": [{"type": "text", "text": prompt}, {"type": "image_url", "image_url": {"url": image_url}}]
    },
]

outputs = vllm_model.model.chat(messages, sampling_params=sampling_params)

print(outputs[0].outputs[0].text)
```

**_Advanced Example_**

You can also pass multiple images per message and/or pass multi-turn conversations

```py
from vllm import LLM
from vllm.sampling_params import SamplingParams
from modelscope import snapshot_download

model_name = snapshot_download("LLM-Research/Pixtral-12B-2409")
max_img_per_msg = 5
max_tokens_per_img = 4096

sampling_params = SamplingParams(max_tokens=8192, temperature=0.7)
llm = LLM(model=model_name, tokenizer_mode="mistral", limit_mm_per_prompt={"image": max_img_per_msg}, max_num_batched_tokens=max_img_per_msg * max_tokens_per_img)

prompt = "Describe the following image."

url_1 = "https://model-demo.oss-cn-hangzhou.aliyuncs.com/demo.jpeg"
url_2 = "https://picsum.photos/seed/picsum/200/300"
url_3 = "https://picsum.photos/id/32/512/512"

messages = [
    {
        "role": "user",
        "content": [{"type": "text", "text": prompt}, {"type": "image_url", "image_url": {"url": url_1}}, {"type": "image_url", "image_url": {"url": url_2}}],
    }
]

outputs = llm.chat(messages=messages, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
```

**_Server_**

You can also use pixtral in a server/client setting. 

1. Spin up a server:

```
vllm serve mistralai/Pixtral-12B-2409 --tokenizer_mode mistral --limit_mm_per_prompt 'image=4' --max_num_batched_tokens 16384
```

2. And ping the client:

```
curl --location 'http://<your-node-url>:8000/v1/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer token' \
--data '{
    "model": "mistralai/Pixtral-12B-2409",
    "messages": [
      {
        "role": "user",
        "content": [
            {"type" : "text", "text": "Describe this image in detail please."},
            {"type": "image_url", "image_url": {"url": "https://s3.amazonaws.com/cms.ipressroom.com/338/files/201808/5b894ee1a138352221103195_A680%7Ejogging-edit/A680%7Ejogging-edit_hero.jpg"}},
            {"type" : "text", "text": "and this one as well. Answer in French."},
            {"type": "image_url", "image_url": {"url": "https://www.wolframcloud.com/obj/resourcesystem/images/a0e/a0ee3983-46c6-4c92-b85d-059044639928/6af8cfb971db031b.png"}}
        ]
      }
    ]
  }'
```