Instructions to use Nanthasit/sakthai-vision-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nanthasit/sakthai-vision-7b with Transformers:

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

pipe = pipeline("image-text-to-text", model="Nanthasit/sakthai-vision-7b")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Nanthasit/sakthai-vision-7b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Nanthasit/sakthai-vision-7b 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 Nanthasit/sakthai-vision-7b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nanthasit/sakthai-vision-7b:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Nanthasit/sakthai-vision-7b:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Nanthasit/sakthai-vision-7b: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 Nanthasit/sakthai-vision-7b:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Nanthasit/sakthai-vision-7b: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 Nanthasit/sakthai-vision-7b:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nanthasit/sakthai-vision-7b:Q4_K_M

Use Docker

docker model run hf.co/Nanthasit/sakthai-vision-7b:Q4_K_M

LM Studio
Jan

vLLM

How to use Nanthasit/sakthai-vision-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nanthasit/sakthai-vision-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nanthasit/sakthai-vision-7b",
		"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/Nanthasit/sakthai-vision-7b:Q4_K_M

SGLang

How to use Nanthasit/sakthai-vision-7b 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 "Nanthasit/sakthai-vision-7b" \
    --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": "Nanthasit/sakthai-vision-7b",
		"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 "Nanthasit/sakthai-vision-7b" \
        --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": "Nanthasit/sakthai-vision-7b",
		"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 Nanthasit/sakthai-vision-7b with Ollama:
```
ollama run hf.co/Nanthasit/sakthai-vision-7b:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use Nanthasit/sakthai-vision-7b with Docker Model Runner:
```
docker model run hf.co/Nanthasit/sakthai-vision-7b:Q4_K_M
```

Lemonade

How to use Nanthasit/sakthai-vision-7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nanthasit/sakthai-vision-7b:Q4_K_M

Run and chat with the model

lemonade run user.sakthai-vision-7b-Q4_K_M

List all available models

lemonade list

Atomic Chat

Nanthasit commited on Jul 31

Commit

fddeb2e

verified ·

1 Parent(s): a85d74f

Fix cross-links: remove dead sakthai-vision-demo Space (404), update sibling ref, fix Spaces count 3→4

Browse files

Files changed (1) hide show

README.md +6 -34

README.md CHANGED Viewed

@@ -28,7 +28,7 @@ tags:
 - safetensors
 base_model: liuhaotian/LLaVA-1.5-7b
 extra:
-  sibling: Nanthasit/sakthai-vision-demo
   formats: GGUF Q4_K_M
 datasets:
 - liuhaotian/LLaVA-Instruct-150K
@@ -42,7 +42,7 @@ datasets:
   <img src="https://img.shields.io/badge/base-LLaVA%201.5%207B-blueviolet" alt="Base"/>
   <img src="https://img.shields.io/badge/GGUF-Q4__K__M-orange" alt="GGUF"/>
   <a href="https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02"><img src="https://img.shields.io/badge/🏠-SakThai%20Family-6644cc" alt="Collection"/></a>
-  <a href="https://huggingface.co/spaces/Nanthasit/sakthai-vision-demo"><img src="https://img.shields.io/badge/🚀-Try%20on%20Spaces-47d147" alt="Spaces"/></a>
   <img src="https://img.shields.io/badge/benchmark-78.5%25%20VQAv2-success?logo=googlechrome" alt="VQAv2"/>
 </p>
@@ -104,38 +104,8 @@ Every download, like, and share tells the algorithm: *this matters.*
 ---
-## Try It Now 🚀
-**[SakThai Vision Demo](https://huggingface.co/spaces/Nanthasit/sakthai-vision-demo)** — upload an image and ask questions about it, right in your browser, no install needed.
----
 ## Pipeline Integration
-```
-                    ┌──────────────────┐
-                    │  User Image      │
-                    └────────┬─────────┘
-                             │
-                             ▼
-                    ┌──────────────────┐
-                    │  SakThai Vision  │
-                    │  7B (this model) │ ────▶ Image description / answer
-                    └────────┬─────────┘
-                             │
-                             ▼
-                    ┌──────────────────┐
-                    │  Context 1.5B/7B │
-                    │  (tool-calling)  │ ────▶ Acts on visual info
-                    └────────┬─────────┘
-                             │
-                             ▼
-                    ┌──────────────────┐
-                    │  TTS Model       │
-                    │  (speech output) │
-                    └──────────────────┘
-```
 | Stage | Model | Role |
 |-------|-------|------|
 | **See** | [SakThai Vision 7B](https://huggingface.co/Nanthasit/sakthai-vision-7b) ⬅ | Image→text, visual QA |
@@ -145,6 +115,8 @@ Every download, like, and share tells the algorithm: *this matters.*
 ---
 ## What It Is
 A **Q4_K_M GGUF** conversion of **LLaVA-1.5-7B** (Vicuna-7B + CLIP ViT-L/14) for local multimodal inference via **llama.cpp**. This is a **repackaging/quantization of upstream LLaVA** — optimized for CPU inference with a ~4 GB footprint.
@@ -333,7 +305,7 @@ ollama run sakthai-vision "What's in this photo?" --image path/to/photo.jpg
 | [embedding-multilingual](https://huggingface.co/Nanthasit/sakthai-embedding-multilingual) | 80 MB | Cross-lingual embeddings |
 | [tts-model](https://huggingface.co/Nanthasit/sakthai-tts-model) | 141 MB | Text-to-speech, 15 langs |
-**17 models in the family · 10 datasets · 3 Spaces** — [full collection →](https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02)
 ## Links
@@ -341,7 +313,7 @@ ollama run sakthai-vision "What's in this photo?" --image path/to/photo.jpg
 [Sak-Family-Agent GitHub](https://github.com/beer-sakthai/Sak-Family-Agent) ·
 [All models](https://huggingface.co/Nanthasit) ·
 [All datasets](https://huggingface.co/Nanthasit?tab=datasets) ·
-[Vision Demo Space](https://huggingface.co/spaces/Nanthasit/sakthai-vision-demo)
 ---

 - safetensors
 base_model: liuhaotian/LLaVA-1.5-7b
 extra:
+  sibling: Nanthasit/sakthai-web-agent
   formats: GGUF Q4_K_M
 datasets:
 - liuhaotian/LLaVA-Instruct-150K
   <img src="https://img.shields.io/badge/base-LLaVA%201.5%207B-blueviolet" alt="Base"/>
   <img src="https://img.shields.io/badge/GGUF-Q4__K__M-orange" alt="GGUF"/>
   <a href="https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02"><img src="https://img.shields.io/badge/🏠-SakThai%20Family-6644cc" alt="Collection"/></a>
+  <a href="https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02"><img src="https://img.shields.io/badge/🚀-Explore%20Family-47d147" alt="Collection"/></a>
   <img src="https://img.shields.io/badge/benchmark-78.5%25%20VQAv2-success?logo=googlechrome" alt="VQAv2"/>
 </p>
 ---
 ## Pipeline Integration
 | Stage | Model | Role |
 |-------|-------|------|
 | **See** | [SakThai Vision 7B](https://huggingface.co/Nanthasit/sakthai-vision-7b) ⬅ | Image→text, visual QA |
 ---
+---
 ## What It Is
 A **Q4_K_M GGUF** conversion of **LLaVA-1.5-7B** (Vicuna-7B + CLIP ViT-L/14) for local multimodal inference via **llama.cpp**. This is a **repackaging/quantization of upstream LLaVA** — optimized for CPU inference with a ~4 GB footprint.
 | [embedding-multilingual](https://huggingface.co/Nanthasit/sakthai-embedding-multilingual) | 80 MB | Cross-lingual embeddings |
 | [tts-model](https://huggingface.co/Nanthasit/sakthai-tts-model) | 141 MB | Text-to-speech, 15 langs |
+**17 models in the family · 10 datasets · 4 Spaces** — [full collection →](https://huggingface.co/collections/Nanthasit/sakthai-model-family-6a64745450b12d421c1f9f02)
 ## Links
 [Sak-Family-Agent GitHub](https://github.com/beer-sakthai/Sak-Family-Agent) ·
 [All models](https://huggingface.co/Nanthasit) ·
 [All datasets](https://huggingface.co/Nanthasit?tab=datasets) ·
+[Web Agent Space](https://huggingface.co/spaces/Nanthasit/sakthai-web-agent)
 ---