Instructions to use VertexAGI/prism-caption-1-micro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VertexAGI/prism-caption-1-micro with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("VertexAGI/prism-caption-1-micro")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use VertexAGI/prism-caption-1-micro with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf VertexAGI/prism-caption-1-micro:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VertexAGI/prism-caption-1-micro:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf VertexAGI/prism-caption-1-micro:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VertexAGI/prism-caption-1-micro:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf VertexAGI/prism-caption-1-micro:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf VertexAGI/prism-caption-1-micro:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf VertexAGI/prism-caption-1-micro:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VertexAGI/prism-caption-1-micro:Q4_K_M

Use Docker

docker model run hf.co/VertexAGI/prism-caption-1-micro:Q4_K_M

LM Studio
Jan

vLLM

How to use VertexAGI/prism-caption-1-micro with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VertexAGI/prism-caption-1-micro"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VertexAGI/prism-caption-1-micro",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VertexAGI/prism-caption-1-micro:Q4_K_M

Ollama
How to use VertexAGI/prism-caption-1-micro with Ollama:
```
ollama run hf.co/VertexAGI/prism-caption-1-micro:Q4_K_M
```

Unsloth Studio

How to use VertexAGI/prism-caption-1-micro with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for VertexAGI/prism-caption-1-micro to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for VertexAGI/prism-caption-1-micro to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for VertexAGI/prism-caption-1-micro to start chatting

MLX LM

How to use VertexAGI/prism-caption-1-micro with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "VertexAGI/prism-caption-1-micro"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "VertexAGI/prism-caption-1-micro"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "VertexAGI/prism-caption-1-micro",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use VertexAGI/prism-caption-1-micro with Docker Model Runner:
```
docker model run hf.co/VertexAGI/prism-caption-1-micro:Q4_K_M
```

Lemonade

How to use VertexAGI/prism-caption-1-micro with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VertexAGI/prism-caption-1-micro:Q4_K_M

Run and chat with the model

lemonade run user.prism-caption-1-micro-Q4_K_M

List all available models

lemonade list

Atomic Chat

prism-caption-1-micro

File size: 1,098 Bytes

8aa89a2

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