Instructions to use ubergarm/Ling-1T-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ubergarm/Ling-1T-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 ubergarm/Ling-1T-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/Ling-1T-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ubergarm/Ling-1T-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf ubergarm/Ling-1T-GGUF:Q2_K

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 ubergarm/Ling-1T-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf ubergarm/Ling-1T-GGUF:Q2_K

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 ubergarm/Ling-1T-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ubergarm/Ling-1T-GGUF:Q2_K

Use Docker

docker model run hf.co/ubergarm/Ling-1T-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use ubergarm/Ling-1T-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ubergarm/Ling-1T-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": "ubergarm/Ling-1T-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ubergarm/Ling-1T-GGUF:Q2_K

Ollama
How to use ubergarm/Ling-1T-GGUF with Ollama:
```
ollama run hf.co/ubergarm/Ling-1T-GGUF:Q2_K
```

Unsloth Studio

How to use ubergarm/Ling-1T-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 ubergarm/Ling-1T-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 ubergarm/Ling-1T-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ubergarm/Ling-1T-GGUF to start chatting

How to use ubergarm/Ling-1T-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Ling-1T-GGUF:Q2_K

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": "ubergarm/Ling-1T-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ubergarm/Ling-1T-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Ling-1T-GGUF:Q2_K

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 ubergarm/Ling-1T-GGUF:Q2_K

Run Hermes

hermes

OpenClaw new

How to use ubergarm/Ling-1T-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ubergarm/Ling-1T-GGUF:Q2_K

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ubergarm/Ling-1T-GGUF:Q2_K" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use ubergarm/Ling-1T-GGUF with Docker Model Runner:
```
docker model run hf.co/ubergarm/Ling-1T-GGUF:Q2_K
```

Lemonade

How to use ubergarm/Ling-1T-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ubergarm/Ling-1T-GGUF:Q2_K

Run and chat with the model

lemonade run user.Ling-1T-GGUF-Q2_K

List all available models

lemonade list

Atomic Chat

Great Work!

by anikifoss - opened Oct 18, 2025

Discussion

anikifoss

Oct 18, 2025

Great works, as usual!

How do you find this model so far?

ubergarm

Owner Oct 18, 2025

How do you find this model so far?

I haven't done too much with it other than basic testing. It seems like it can code okay, and rumors I hear are:

From the Openrouter discord, it sounds like it could be good for coding and STEM knowledge, but is slightly broken (possibly the model itself, but more likely some backend bug).
Looks like they are trying to fix it: https://huggingface.co/inclusionAI/Ling-1T/discussions/7#68efdf17ea21691631f93c54

I have run it up to around 40k context depth in 6 turns or so having it process a journal article and asking for pseudo code then finally a c++ diff patch which partially applied but of course wasn't right but it didn't have any "catastrophic" failures it always was cogent... But probably needs more testing...

It has a really low absolute perplexity, which suggests it has memoized english wiki.test.raw well i suppose maybe but kind of interesting - the Q8_0 is under 2.0 perplexity fwiw.

Had some discussion on discord about it too and also the related bailing_moe models like Ling-1T and the flash version: https://www.reddit.com/r/LocalLLaMA/comments/1o9g4if/comment/nk2ieak/

Curious to hear what others think about its coding and also role play abilities etc.

ubergarm

Owner Oct 18, 2025

Oh check this out, its doing well on some benchmarks and working with coding agent stuff: https://huggingface.co/ubergarm2/Ling-1T-GGUF/discussions/1#68f311b69a34602b1a496f39

anikifoss

Oct 18, 2025

Thanks for sharing the bleeding edge updates, this model appears to be more than just hype! I'm excited to see if it can best DeepSeek at coding.

ubergarm

Owner Oct 18, 2025

I'm hoping to release some larger ik specific quants soon and get some perplexity data and chart going soon. I'll likely keep the attn/first 4 dense/shexp full q8_0 for some of the bigger iq5_k type quants if I make that big boi.

anikifoss

Oct 18, 2025

I'll wait for your quants... not enough SSD space on my setup to efficiently deal with 2TB sized FP16 GGUFs.

ubergarm

Owner Oct 18, 2025

The big one is almost finished uploading and perplexity data graph now up too. I know you often like no imatrix, but this particular one has imatrix data used for all tensors. But should at least give a good feel for the model!

And right, I'm almost out of disk space on a couple big RAID arrays already 😅

geveent

Oct 23, 2025

•

edited Oct 23, 2025

@ubergarm Thank you so much John! Without your help, I won't be able to run models like Ling 1T. I am running Ling 1T smol-IQ4_KSS (TG speed at 12t/s). I need to spend more time with the model, but so far it seems to be a very smart model.

I asked, "In Han Kang's "Vegetarian", how many narrators are there in the Novel?". Not many models get this right, but Ling gave me a correct answer.

I also asked, "Tell me about the temporal summation in the context of neuroscience and concentration.". When I ask this to Kimi K2, Kimi K2 answers my question as if I am a subject matter expert. When I ask this to DeepSeek, DeepSeek answers as if I know nothing about the subject. Ling's answer is in the middle. Ling's answer is somewhat technical, but not as extreme as Kimi K2. Kimi K2 is quirky, and that is why it's charming, but for reliable answers I think I will use DeepSeek or Ling.

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