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

Libraries

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

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

pipe = pipeline("text-generation", model="webAI-Official/TwIL-LM3-Pro")
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-Pro")
model = AutoModelForCausalLM.from_pretrained("webAI-Official/TwIL-LM3-Pro", 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-Pro 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-Pro:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf webAI-Official/TwIL-LM3-Pro: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-Pro:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf webAI-Official/TwIL-LM3-Pro: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-Pro:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf webAI-Official/TwIL-LM3-Pro: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-Pro:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf webAI-Official/TwIL-LM3-Pro:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

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

Use Docker

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

SGLang

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

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

How to use webAI-Official/TwIL-LM3-Pro 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-Pro:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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-Pro:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use webAI-Official/TwIL-LM3-Pro with Docker Model Runner:
```
docker model run hf.co/webAI-Official/TwIL-LM3-Pro:Q4_K_M
```

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.TwIL-LM3-Pro-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use webAI-Official/TwIL-LM3-Pro 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-Pro: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-Pro:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use webAI-Official/TwIL-LM3-Pro 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-Pro: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-Pro: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"

anurag051194 commited on 2 days ago

Commit

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verified ·

1 Parent(s): 588354c

Update README.md

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README.md CHANGED Viewed

@@ -480,13 +480,13 @@ The one setting that is chosen per model is the merge coefficient, picked by a s
 performance: the best WiSE-FT weight is 0.15 for SmolLM3, 0.20 for Granite and the TwIL base, and
 0.50 for VibeThinker-3B, which needs to keep far more of the tuned weights than the others do.
-| base model | best tuned configuration | Track A macro gate (base → tuned) | Track B 10-dataset macro (base → tuned) |
-|---|---|---:|---:|
-| Granite | WiSE-FT λ = 0.20 | 0.309 → 0.454 | 0.777 → 0.786 |
-| SmolLM3 | WiSE-FT λ = 0.15 | 0.344 → 0.446 | 0.723 → 0.736 |
-| TwIL base | WiSE-FT λ = 0.20 | 0.423 → 0.435 | 0.729 → 0.716 |
-| VibeThinker-3B | WiSE-FT λ = 0.50 | 0.374 → 0.541 | 0.815 → 0.802 |
-| Granite 4.2 (this model) | MGPO β = 0.02, step 2580 | 0.4313 → 0.5539 | 0.7942 → 0.7901 |
 Track A improves on every base, from +0.012 to +0.167, and the Track B change stays within ±0.013,
 inside sampling noise at n = 300 per dataset. The first four rows are from the family comparison

 performance: the best WiSE-FT weight is 0.15 for SmolLM3, 0.20 for Granite and the TwIL base, and
 0.50 for VibeThinker-3B, which needs to keep far more of the tuned weights than the others do.
+| base model | Track A macro gate (base → tuned) | Track B 10-dataset macro (base → tuned) |
+|---|---:|---:|
+| Granite | 0.309 → 0.454 | 0.777 → 0.786 |
+| SmolLM3 | 0.344 → 0.446 | 0.723 → 0.736 |
+| TwIL base | 0.423 → 0.435 | 0.729 → 0.716 |
+| VibeThinker-3B | 0.374 → 0.541 | 0.810 → 0.802 |
+| Granite 4.2 (this model)| 0.4313 → 0.5539 | 0.7942 → 0.7901 |
 Track A improves on every base, from +0.012 to +0.167, and the Track B change stays within ±0.013,
 inside sampling noise at n = 300 per dataset. The first four rows are from the family comparison