Instructions to use webAI-Official/TwIL-LM3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use webAI-Official/TwIL-LM3 with Transformers:

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

pipe = pipeline("text-generation", model="webAI-Official/TwIL-LM3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("webAI-Official/TwIL-LM3")
model = AutoModelForCausalLM.from_pretrained("webAI-Official/TwIL-LM3", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use webAI-Official/TwIL-LM3 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 webAI-Official/TwIL-LM3:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf webAI-Official/TwIL-LM3:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf webAI-Official/TwIL-LM3:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf webAI-Official/TwIL-LM3: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 webAI-Official/TwIL-LM3:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf webAI-Official/TwIL-LM3: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 webAI-Official/TwIL-LM3:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf webAI-Official/TwIL-LM3:Q4_K_M

Use Docker

docker model run hf.co/webAI-Official/TwIL-LM3:Q4_K_M

LM Studio
Jan

vLLM

How to use webAI-Official/TwIL-LM3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "webAI-Official/TwIL-LM3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "webAI-Official/TwIL-LM3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/webAI-Official/TwIL-LM3:Q4_K_M

SGLang

How to use webAI-Official/TwIL-LM3 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 "webAI-Official/TwIL-LM3" \
    --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": "webAI-Official/TwIL-LM3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "webAI-Official/TwIL-LM3" \
        --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": "webAI-Official/TwIL-LM3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use webAI-Official/TwIL-LM3 with Ollama:
```
ollama run hf.co/webAI-Official/TwIL-LM3:Q4_K_M
```

Unsloth Studio

How to use webAI-Official/TwIL-LM3 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 webAI-Official/TwIL-LM3 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 webAI-Official/TwIL-LM3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for webAI-Official/TwIL-LM3 to start chatting

How to use webAI-Official/TwIL-LM3 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf webAI-Official/TwIL-LM3: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": "webAI-Official/TwIL-LM3:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use webAI-Official/TwIL-LM3 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf webAI-Official/TwIL-LM3:Q4_K_M

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 "webAI-Official/TwIL-LM3:Q4_K_M" \
  --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 webAI-Official/TwIL-LM3 with Docker Model Runner:
```
docker model run hf.co/webAI-Official/TwIL-LM3:Q4_K_M
```

Lemonade

How to use webAI-Official/TwIL-LM3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull webAI-Official/TwIL-LM3:Q4_K_M

Run and chat with the model

lemonade run user.TwIL-LM3-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use webAI-Official/TwIL-LM3 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf webAI-Official/TwIL-LM3: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 webAI-Official/TwIL-LM3:Q4_K_M

Run Hermes

hermes

Atomic Chat

benchmaxxed ?

by mrfakename - opened about 10 hours ago

Discussion

mrfakename

about 10 hours ago

•

edited about 10 hours ago

curious what ppl's actual experiences are with this model // next phi ?

anurag051194

webAI org about 9 hours ago

•

edited about 9 hours ago

This is interesting. But in our case, we took a pretrained model, and we did not test on the training data. Our model uses SmoL-LM as the baseline and we were able to provide appropirate comparisons to the base model, which demonstrates that there was no data contamination. Our training pipeline included extensive two step data cleaning and verification along with a synthetic data generation framework, fine tuning with several diversity induced fusion and merging, and an extensive RLVR stage. We also used some concepts from continual learning to make sure that the model does not forget previous knowledge while learning new tasks. We are not winning on every benchmark and there have been recent models which have been able to show even better performance on multiple benchmarks. Check VibeThinker or LFM (they do better than Opus or GPT-4.5 on some other benchmarks) for instance. The bottomline is, we are confident that there was no data contamination because we never pretrained on any seen knowledge corpus.

mrfakename

about 9 hours ago

very cool !! would be interested to see how this model performs in real-world testing

anurag051194

webAI org about 8 hours ago

Yes, we are also testing it currently and have found some interesting applications. This might be helpful to visualize.
https://www.linkedin.com/posts/catherine-ordun_im-pretty-excited-to-show-a-demo-of-webais-ugcPost-7492702828478693377-Jzm9/?utm_source=social_share_send&utm_medium=android_app&rcm=ACoAAB2xny0B0N0BU_KZIibAgaxSe-6ha6zJNy0&utm_campaign=copy_link

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