Instructions to use internlm/Intern-S1-mini-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use internlm/Intern-S1-mini-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="internlm/Intern-S1-mini-GGUF",
	filename="Q8_0/Intern-S1-mini-Q8_0.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 internlm/Intern-S1-mini-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf internlm/Intern-S1-mini-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf internlm/Intern-S1-mini-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf internlm/Intern-S1-mini-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf internlm/Intern-S1-mini-GGUF:Q8_0

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 internlm/Intern-S1-mini-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf internlm/Intern-S1-mini-GGUF:Q8_0

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 internlm/Intern-S1-mini-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf internlm/Intern-S1-mini-GGUF:Q8_0

Use Docker

docker model run hf.co/internlm/Intern-S1-mini-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use internlm/Intern-S1-mini-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "internlm/Intern-S1-mini-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "internlm/Intern-S1-mini-GGUF",
		"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/internlm/Intern-S1-mini-GGUF:Q8_0

Ollama
How to use internlm/Intern-S1-mini-GGUF with Ollama:
```
ollama run hf.co/internlm/Intern-S1-mini-GGUF:Q8_0
```

Unsloth Studio

How to use internlm/Intern-S1-mini-GGUF 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 internlm/Intern-S1-mini-GGUF 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 internlm/Intern-S1-mini-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for internlm/Intern-S1-mini-GGUF to start chatting

How to use internlm/Intern-S1-mini-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf internlm/Intern-S1-mini-GGUF:Q8_0

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": "internlm/Intern-S1-mini-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use internlm/Intern-S1-mini-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf internlm/Intern-S1-mini-GGUF:Q8_0

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 internlm/Intern-S1-mini-GGUF:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use internlm/Intern-S1-mini-GGUF with Docker Model Runner:
```
docker model run hf.co/internlm/Intern-S1-mini-GGUF:Q8_0
```

Lemonade

How to use internlm/Intern-S1-mini-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull internlm/Intern-S1-mini-GGUF:Q8_0

Run and chat with the model

lemonade run user.Intern-S1-mini-GGUF-Q8_0

List all available models

lemonade list

unsubscribe commited on Aug 19, 2025

Commit

242bbd5

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1 Parent(s): 5c03dbb

Update README.md

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README.md +8 -8

README.md CHANGED Viewed

@@ -12,7 +12,7 @@ tags:
 - chat
 ---
-# Intern-S1-GGUF Model
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642695e5274e7ad464c8a5ba/E43cgEXBRWjVJlU_-hdh6.png)
@@ -23,7 +23,7 @@ tags:
 ## Introduction
-The `Intern-S1` model in GGUF format can be utilized by [llama.cpp](https://github.com/ggerganov/llama.cpp), a highly popular open-source framework for Large Language Model (LLM) inference, across a variety of hardware platforms, both locally and in the cloud.
 This repository offers `Intern-S1-mini` models in GGUF format in both half precision and various low-bit quantized versions, including `q8_0`.
 In the subsequent sections, we will first present the installation procedure, followed by an explanation of the model download process.
@@ -77,8 +77,8 @@ Here is an example of using the thinking system prompt.
 system_prompt="<|im_start|>system\nYou are an expert reasoner with extensive experience in all areas. You approach problems through systematic thinking and rigorous reasoning. Your response should reflect deep understanding and precise logical thinking, making your solution path and reasoning clear to others. Please put your thinking process within <think>...</think> tags.\n<|im_end|>\n"
 build/bin/llama-mtmd-cli \
-    --model Intern-S1-GGUF/f16/Intern-S1-mini-f16.gguf  \
-    --mmproj Intern-S1-GGUF/f16/mmproj-Intern-S1-mini-f16.gguf \
     --predict 2048 \
     --ctx-size 8192 \
     --gpu-layers 100 \
@@ -96,8 +96,8 @@ Then input your question with image input as `/image xxx.jpg`.
 ```shell
 ./build/bin/llama-server \
-    --model Intern-S1-GGUF/f16/Intern-S1-mini-f16.gguf \
-    --mmproj Intern-S1-GGUF/f16/mmproj-Intern-S1-mini-f16.gguf \
     --gpu-layers 100 \
     --temp 0.8 \
     --top-p 0.8 \
@@ -135,8 +135,8 @@ print(response)
 # install ollama
 curl -fsSL https://ollama.com/install.sh | sh
 # fetch model
-ollama pull internlm/interns1
 # run model
-ollama run internlm/interns1
 # then use openai client to call on http://localhost:11434/v1
 ```

 - chat
 ---
+# Intern-S1-mini-GGUF Model
 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/642695e5274e7ad464c8a5ba/E43cgEXBRWjVJlU_-hdh6.png)
 ## Introduction
+The `Intern-S1-mini` model in GGUF format can be utilized by [llama.cpp](https://github.com/ggerganov/llama.cpp), a highly popular open-source framework for Large Language Model (LLM) inference, across a variety of hardware platforms, both locally and in the cloud.
 This repository offers `Intern-S1-mini` models in GGUF format in both half precision and various low-bit quantized versions, including `q8_0`.
 In the subsequent sections, we will first present the installation procedure, followed by an explanation of the model download process.
 system_prompt="<|im_start|>system\nYou are an expert reasoner with extensive experience in all areas. You approach problems through systematic thinking and rigorous reasoning. Your response should reflect deep understanding and precise logical thinking, making your solution path and reasoning clear to others. Please put your thinking process within <think>...</think> tags.\n<|im_end|>\n"
 build/bin/llama-mtmd-cli \
+    --model Intern-S1-mini-GGUF/f16/Intern-S1-mini-f16.gguf  \
+    --mmproj Intern-S1-mini-GGUF/f16/mmproj-Intern-S1-mini-f16.gguf \
     --predict 2048 \
     --ctx-size 8192 \
     --gpu-layers 100 \
 ```shell
 ./build/bin/llama-server \
+    --model Intern-S1-mini-GGUF/f16/Intern-S1-mini-f16.gguf \
+    --mmproj Intern-S1-mini-GGUF/f16/mmproj-Intern-S1-mini-f16.gguf \
     --gpu-layers 100 \
     --temp 0.8 \
     --top-p 0.8 \
 # install ollama
 curl -fsSL https://ollama.com/install.sh | sh
 # fetch model
+ollama pull internlm/interns1:mini
 # run model
+ollama run internlm/interns1:mini
 # then use openai client to call on http://localhost:11434/v1
 ```