Instructions to use Lonelyguyse1/halide-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lonelyguyse1/halide-vision with Transformers:

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

pipe = pipeline("image-text-to-text", model="Lonelyguyse1/halide-vision")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Lonelyguyse1/halide-vision")
model = AutoModelForMultimodalLM.from_pretrained("Lonelyguyse1/halide-vision")
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 = processor.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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use Lonelyguyse1/halide-vision with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Lonelyguyse1/halide-vision",
	filename="minicpm-v-4.6-merged-v3-q4_k_m.gguf",
)

llm.create_chat_completion(
	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"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Lonelyguyse1/halide-vision with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Lonelyguyse1/halide-vision:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Lonelyguyse1/halide-vision:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Lonelyguyse1/halide-vision:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Lonelyguyse1/halide-vision: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 Lonelyguyse1/halide-vision:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Lonelyguyse1/halide-vision: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 Lonelyguyse1/halide-vision:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lonelyguyse1/halide-vision:Q4_K_M

Use Docker

docker model run hf.co/Lonelyguyse1/halide-vision:Q4_K_M

LM Studio
Jan

vLLM

How to use Lonelyguyse1/halide-vision with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lonelyguyse1/halide-vision"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lonelyguyse1/halide-vision",
		"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/Lonelyguyse1/halide-vision:Q4_K_M

SGLang

How to use Lonelyguyse1/halide-vision 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 "Lonelyguyse1/halide-vision" \
    --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": "Lonelyguyse1/halide-vision",
		"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 "Lonelyguyse1/halide-vision" \
        --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": "Lonelyguyse1/halide-vision",
		"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"
						}
					}
				]
			}
		]
	}'

Ollama
How to use Lonelyguyse1/halide-vision with Ollama:
```
ollama run hf.co/Lonelyguyse1/halide-vision:Q4_K_M
```

Unsloth Studio

How to use Lonelyguyse1/halide-vision 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 Lonelyguyse1/halide-vision 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 Lonelyguyse1/halide-vision to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Lonelyguyse1/halide-vision to start chatting

How to use Lonelyguyse1/halide-vision with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Lonelyguyse1/halide-vision:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Lonelyguyse1/halide-vision:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Lonelyguyse1/halide-vision with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Lonelyguyse1/halide-vision:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Lonelyguyse1/halide-vision:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Lonelyguyse1/halide-vision with Docker Model Runner:
```
docker model run hf.co/Lonelyguyse1/halide-vision:Q4_K_M
```

Lemonade

How to use Lonelyguyse1/halide-vision with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lonelyguyse1/halide-vision:Q4_K_M

Run and chat with the model

lemonade run user.halide-vision-Q4_K_M

List all available models

lemonade list

Halide Vision

Halide Vision is a MiniCPM-V 4.6 checkpoint fine-tuned for analog film-scan defect extraction. It is maintained by Lonelyguyse1 for Project Halide.

The model emits JSON defect proposals for dust, dirt, scratches, hair-like surface contamination, emulsion damage, chemical stains, and light leaks. The Project Halide runtime validates the JSON schema, removes low-confidence or duplicate boxes, and uses tiled inspection when large scans hide thin crack networks at full-frame scale.

Training Summary

Base model: openbmb/MiniCPM-V-4.6
Training method: LoRA fine-tuning with LLaMA-Factory, merged for inference
Curriculum: FilmDamageSimulator annotations, procedural film-defect positives, hard clean negatives, and a v7 crack curriculum
Held-out private negatives: used only for evaluation, not for training

Held-Out Smoke Result

Final v7 checkpoint with 960 px tiled fallback:

Sample	Expected surface condition	Result
negative1	Long scratches across portrait	8 defects
negative2	Abraded emulsion and dirt patches	9 defects
negative3	Severe emulsion damage and debris	6 defects
negative4	Near-clean hard negative	0 defects
negative5	Broad lifted crack network	45 defects

Runtime Notes

This model is intended to run inside Project Halide with GPU inference. The runtime refuses local CPU model inference and does not call cloud inference APIs.

The repo also includes a llama.cpp Q4_K_M GGUF artifact: minicpm-v-4.6-merged-v7-crack-curriculum-r1-ckpt625-q4_k_m.gguf.

Use the model as an inspection aid. It can over-box broad damage regions, and film metadata should be treated as context unless verified by notes or edge marks.

Downloads last month: 118

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for Lonelyguyse1/halide-vision

Base model

openbmb/MiniCPM-V-4.6

Quantized

(23)

this model

Lonelyguyse1
/

halide-vision

Halide Vision

Training Summary

Held-Out Smoke Result

Runtime Notes

Model tree for Lonelyguyse1/halide-vision

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