Instructions to use SeanScripts/pixtral-12b-nf4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SeanScripts/pixtral-12b-nf4 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="SeanScripts/pixtral-12b-nf4")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("SeanScripts/pixtral-12b-nf4")
model = AutoModelForImageTextToText.from_pretrained("SeanScripts/pixtral-12b-nf4")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SeanScripts/pixtral-12b-nf4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SeanScripts/pixtral-12b-nf4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SeanScripts/pixtral-12b-nf4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SeanScripts/pixtral-12b-nf4

SGLang

How to use SeanScripts/pixtral-12b-nf4 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SeanScripts/pixtral-12b-nf4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SeanScripts/pixtral-12b-nf4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SeanScripts/pixtral-12b-nf4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SeanScripts/pixtral-12b-nf4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SeanScripts/pixtral-12b-nf4 with Docker Model Runner:
```
docker model run hf.co/SeanScripts/pixtral-12b-nf4
```

pixtral-12b-nf4 / README.md

SeanScripts

Update README.md

ce4c79e verified over 1 year ago

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	---
	license: apache-2.0
	base_model:
	- mistral-community/pixtral-12b
	pipeline_tag: image-text-to-text
	library_name: transformers
	---

	Converted from [mistral-community/pixtral-12b](https://huggingface.co/mistral-community/pixtral-12b) using BitsAndBytes with NF4 (4-bit) quantization. Not using double quantization.
	Requires `bitsandbytes` to load.

	Example usage for image captioning:
	```python
	from transformers import LlavaForConditionalGeneration, AutoProcessor, BitsAndBytesConfig
	from PIL import Image
	import time

	# Load model
	model_id = "SeanScripts/pixtral-12b-nf4"
	model = LlavaForConditionalGeneration.from_pretrained(
	model_id,
	use_safetensors=True,
	device_map="cuda:0"
	)
	# Load tokenizer
	processor = AutoProcessor.from_pretrained(model_id)

	# Caption a local image
	IMG_URLS = [Image.open("test.png").convert("RGB")]
	PROMPT = "<s>[INST]Caption this image:\n[IMG][/INST]"

	inputs = processor(images=IMG_URLS, text=PROMPT, return_tensors="pt").to("cuda")
	prompt_tokens = len(inputs['input_ids'][0])
	print(f"Prompt tokens: {prompt_tokens}")

	t0 = time.time()
	generate_ids = model.generate(**inputs, max_new_tokens=512)
	t1 = time.time()
	total_time = t1 - t0
	generated_tokens = len(generate_ids[0]) - prompt_tokens
	time_per_token = generated_tokens/total_time
	print(f"Generated {generated_tokens} tokens in {total_time:.3f} s ({time_per_token:.3f} tok/s)")

	output = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
	print(output)
	```

	On a 4090, this is getting about 10 - 12 tok/s (without flash attention) and the captions seem pretty good, though I haven't tested very many. It uses about 10 GB VRAM.

	You can get a set of ComfyUI custom nodes for running this model here:
	[https://github.com/SeanScripts/ComfyUI-PixtralLlamaVision](https://github.com/SeanScripts/ComfyUI-PixtralLlamaVision)