Instructions to use solidrust/Mixtral_AI_MiniTronVision-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use solidrust/Mixtral_AI_MiniTronVision-AWQ with Transformers:

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

pipe = pipeline("image-text-to-text", model="solidrust/Mixtral_AI_MiniTronVision-AWQ")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("solidrust/Mixtral_AI_MiniTronVision-AWQ")
model = AutoModelForCausalLM.from_pretrained("solidrust/Mixtral_AI_MiniTronVision-AWQ", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use solidrust/Mixtral_AI_MiniTronVision-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "solidrust/Mixtral_AI_MiniTronVision-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/Mixtral_AI_MiniTronVision-AWQ",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/solidrust/Mixtral_AI_MiniTronVision-AWQ

SGLang

How to use solidrust/Mixtral_AI_MiniTronVision-AWQ 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 "solidrust/Mixtral_AI_MiniTronVision-AWQ" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/Mixtral_AI_MiniTronVision-AWQ",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "solidrust/Mixtral_AI_MiniTronVision-AWQ" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "solidrust/Mixtral_AI_MiniTronVision-AWQ",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use solidrust/Mixtral_AI_MiniTronVision-AWQ with Docker Model Runner:
```
docker model run hf.co/solidrust/Mixtral_AI_MiniTronVision-AWQ
```

Suparious commited on Apr 21, 2024

Commit

464125c

verified ·

1 Parent(s): 3f817ee

Update README.md

Browse files

Files changed (1) hide show

README.md +33 -2

README.md CHANGED Viewed

@@ -1,4 +1,6 @@
 ---
 library_name: transformers
 tags:
 - 4-bit
@@ -6,10 +8,39 @@ tags:
 - text-generation
 - autotrain_compatible
 - endpoints_compatible
-pipeline_tag: text-generation
 inference: false
 quantized_by: Suparious
 ---
 #
-**UPLOAD IN PROGRESS**

 ---
+language:
+- en
 library_name: transformers
 tags:
 - 4-bit
 - text-generation
 - autotrain_compatible
 - endpoints_compatible
+- vision
+pipeline_tag: image-text-to-text
 inference: false
 quantized_by: Suparious
 ---
 #
+## Model Summary
+# ADD HEAD
+```
+print('Add Vision...')
+# ADD HEAD
+# Combine pre-trained encoder and pre-trained decoder to form a Seq2Seq model
+Vmodel = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
+    "google/vit-base-patch16-224-in21k", "LeroyDyer/Mixtral_AI_Tiny"
+)
+_Encoder_ImageProcessor = Vmodel.encoder
+_Decoder_ImageTokenizer = Vmodel.decoder
+_VisionEncoderDecoderModel = Vmodel
+# Add Pad tokems
+LM_MODEL.VisionEncoderDecoder = _VisionEncoderDecoderModel
+# Add Sub Components
+LM_MODEL.Encoder_ImageProcessor = _Encoder_ImageProcessor
+LM_MODEL.Decoder_ImageTokenizer = _Decoder_ImageTokenizer
+LM_MODEL
+```