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

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

OpenClaw

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"

anurag051194 commited on 13 days ago

Commit

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1 Parent(s): 76f52bc

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@@ -37,54 +37,6 @@ why it is the recommended release of the pair.
 ### Track A — in-domain formal logic
-The **macro gate** is the mean of five objective scores: entailment labelling, multiple-choice
-answering, procedural reasoning, Lean proof critique, and rule induction (scored by its
-continuous derivation score). MCQ and procedural are credited as `max(exact_match, loose_match)`.
-`n = 200` prompts per objective, greedy decoding, 2048 max new tokens.
-| objective | SmolLM3-3B | TwIL-LM3 | Δ |
-|---|---:|---:|---:|
-| rule_induction | 0.103 | **0.319** | +0.216 |
-| entailment_label | 0.335 | **0.575** | +0.240 |
-| lean_critic | 0.630 | **0.660** | +0.030 |
-| procedural | 0.105 | 0.110 | +0.005 |
-| mcq_answer | 0.505 | 0.445 | −0.060 |
-| **macro gate** | **0.3356** | **0.4218** | **+0.0862** |
-Four of five objectives improve. MCQ answering regressed by six points, and that loss is
-averaged into the macro above rather than excluded.
-### Track B — held-out benchmarks
-Nothing in this suite was trained on. Scores are re-derived from saved generations with
-delimiter-aware answer extractors rather than read from harness metrics.
-| | SmolLM3-3B | TwIL-LM3 | Δ |
-|---|---:|---:|---:|
-| core average | 0.790 | **0.812** | **+0.022** |
-| suite average (14 datasets) | 0.661 | **0.669** | **+0.008** |
-This model **passes the per-capability floor**: no core or held-out transfer metric drops by
-more than the 0.02 tolerance against its base.
-Per-dataset, largest moves in each direction:
-| dataset | base | TwIL-LM3 | Δ |
-|---|---:|---:|---:|
-| LogicBench BQA | 0.647 | 0.717 | +0.070 |
-| DROP | 0.700 | 0.747 | +0.047 |
-| CommonsenseQA | 0.707 | 0.737 | +0.030 |
-| StrategyQA | 0.633 | 0.650 | +0.017 |
-| MMLU-Redux | 0.663 | 0.667 | +0.003 |
-| GSM8K | 0.883 | 0.873 | −0.010 |
-| MATH-500 | 0.700 | 0.690 | −0.010 |
-| IFEval (strict) | 0.677 | 0.643 | −0.033 |
-Every regression is within 0.033, and the gains on logical-reasoning transfer tasks
-(LogicBench +0.070, DROP +0.047) are larger than any loss. IFEval is the one place worth
-noting — instruction-following degrades slightly, which is a common cost of verifier-driven RL.
-### Comparison against other open models
 All arms below were run through the same harness, prompts and decoding settings described under
 [Evaluation protocol](#evaluation-protocol). Throughput rows are reported because in-domain score

 ### Track A — in-domain formal logic
 All arms below were run through the same harness, prompts and decoding settings described under
 [Evaluation protocol](#evaluation-protocol). Throughput rows are reported because in-domain score