Instructions to use second-state/internlm3-8b-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use second-state/internlm3-8b-instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="second-state/internlm3-8b-instruct-GGUF",
	filename="internlm3-8b-instruct-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use second-state/internlm3-8b-instruct-GGUF 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 second-state/internlm3-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/internlm3-8b-instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf second-state/internlm3-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/internlm3-8b-instruct-GGUF: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 second-state/internlm3-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/internlm3-8b-instruct-GGUF: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 second-state/internlm3-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/internlm3-8b-instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/internlm3-8b-instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use second-state/internlm3-8b-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "second-state/internlm3-8b-instruct-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": "second-state/internlm3-8b-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/second-state/internlm3-8b-instruct-GGUF:Q4_K_M

Ollama
How to use second-state/internlm3-8b-instruct-GGUF with Ollama:
```
ollama run hf.co/second-state/internlm3-8b-instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use second-state/internlm3-8b-instruct-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 second-state/internlm3-8b-instruct-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 second-state/internlm3-8b-instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for second-state/internlm3-8b-instruct-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use second-state/internlm3-8b-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/internlm3-8b-instruct-GGUF:Q4_K_M
```

Lemonade

How to use second-state/internlm3-8b-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/internlm3-8b-instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.internlm3-8b-instruct-GGUF-Q4_K_M

List all available models

lemonade list

apepkuss79 commited on Jan 17, 2025

Commit

3ab54c7

verified ·

1 Parent(s): c58791c

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +13 -13

README.md CHANGED Viewed

@@ -77,18 +77,18 @@ quantized_by: Second State Inc.
 | Name | Quant method | Bits | Size | Use case |
 | ---- | ---- | ---- | ---- | ----- |
-| [internlm3-8b-instruct-Q2_K.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q2_K.gguf)     | Q2_K   | 2 | 3.01 GB| smallest, significant quality loss - not recommended for most purposes |
-| [internlm3-8b-instruct-Q3_K_L.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q3_K_L.gguf) | Q3_K_L | 3 | 4.13 GB| small, substantial quality loss |
-| [internlm3-8b-instruct-Q3_K_M.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q3_K_M.gguf) | Q3_K_M | 3 | 3.83 GB| very small, high quality loss |
-| [internlm3-8b-instruct-Q3_K_S.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q3_K_S.gguf) | Q3_K_S | 3 | 3.48 GB| very small, high quality loss |
-| [internlm3-8b-instruct-Q4_0.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q4_0.gguf)     | Q4_0   | 4 | 4.45 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
-| [internlm3-8b-instruct-Q4_K_M.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q4_K_M.gguf) | Q4_K_M | 4 | 4.71 GB| medium, balanced quality - recommended |
-| [internlm3-8b-instruct-Q4_K_S.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q4_K_S.gguf) | Q4_K_S | 4 | 4.48 GB| small, greater quality loss |
-| [internlm3-8b-instruct-Q5_0.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q5_0.gguf)     | Q5_0   | 5 | 5.37 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
-| [internlm3-8b-instruct-Q5_K_M.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q5_K_M.gguf) | Q5_K_M | 5 | 5.51 GB| large, very low quality loss - recommended |
-| [internlm3-8b-instruct-Q5_K_S.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q5_K_S.gguf) | Q5_K_S | 5 | 5.37 GB| large, low quality loss - recommended |
-| [internlm3-8b-instruct-Q6_K.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q6_K.gguf)     | Q6_K   | 6 | 6.35 GB| very large, extremely low quality loss |
-| [internlm3-8b-instruct-Q8_0.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q8_0.gguf)     | Q8_0   | 8 | 8.22 GB| very large, extremely low quality loss - not recommended |
-| [internlm3-8b-instruct-f16.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-f16.gguf)       | f16    | 16 | 15.5 GB|  |
 *Quantized with llama.cpp b4497*

 | Name | Quant method | Bits | Size | Use case |
 | ---- | ---- | ---- | ---- | ----- |
+| [internlm3-8b-instruct-Q2_K.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q2_K.gguf)     | Q2_K   | 2 | 3.45 GB| smallest, significant quality loss - not recommended for most purposes |
+| [internlm3-8b-instruct-Q3_K_L.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q3_K_L.gguf) | Q3_K_L | 3 | 4.73 GB| small, substantial quality loss |
+| [internlm3-8b-instruct-Q3_K_M.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q3_K_M.gguf) | Q3_K_M | 3 | 4.39 GB| very small, high quality loss |
+| [internlm3-8b-instruct-Q3_K_S.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q3_K_S.gguf) | Q3_K_S | 3 | 3.99 GB| very small, high quality loss |
+| [internlm3-8b-instruct-Q4_0.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q4_0.gguf)     | Q4_0   | 4 | 5.09 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
+| [internlm3-8b-instruct-Q4_K_M.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q4_K_M.gguf) | Q4_K_M | 4 | 5.36 GB| medium, balanced quality - recommended |
+| [internlm3-8b-instruct-Q4_K_S.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q4_K_S.gguf) | Q4_K_S | 4 | 5.12 GB| small, greater quality loss |
+| [internlm3-8b-instruct-Q5_0.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q5_0.gguf)     | Q5_0   | 5 | 6.13 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
+| [internlm3-8b-instruct-Q5_K_M.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q5_K_M.gguf) | Q5_K_M | 5 | 6.26 GB| large, very low quality loss - recommended |
+| [internlm3-8b-instruct-Q5_K_S.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q5_K_S.gguf) | Q5_K_S | 5 | 6.13 GB| large, low quality loss - recommended |
+| [internlm3-8b-instruct-Q6_K.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q6_K.gguf)     | Q6_K   | 6 | 7.23 GB| very large, extremely low quality loss |
+| [internlm3-8b-instruct-Q8_0.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-Q8_0.gguf)     | Q8_0   | 8 | 9.36 GB| very large, extremely low quality loss - not recommended |
+| [internlm3-8b-instruct-f16.gguf](https://huggingface.co/second-state/internlm3-8b-instruct-GGUF/blob/main/internlm3-8b-instruct-f16.gguf)       | f16    | 16 | 17.6 GB|  |
 *Quantized with llama.cpp b4497*