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

dee09d0

verified ·

1 Parent(s): fdff727

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -6

README.md CHANGED Viewed

@@ -113,9 +113,6 @@ length`, so it measures completed answers rather than raw decode rate.
 | mean gen length | **564** | 999 | 696 | 2296 | 1830 |
 | **ans/s** | **28.1** | 15.6 | 23.2 | 10.9 | 12.0 |
-† The base column here comes from the external-comparison run rather than the paired run used
-for the Δ table above, hence 0.3466 against 0.3356 — run-to-run variation of the same
-checkpoint. The paired run is the correct basis for the improvement claim.
 `strict-7` is the mean of seven lanes scored under strict metrics only (`fol_translation`,
 `entailment_label`, `mcq_answer`, `semantic_parse` and `lean_formalize` exact match,
@@ -167,9 +164,7 @@ Lengths marked ≈ are derived from stored generations using each model's charac
 ratio rather than re-tokenized directly; the method reproduces the three directly measured
 lengths to within 3.5%.
-The honest summary of this table is that TwIL-LM3 does not lead it. Larger models score higher,
-in order of size, and the 120B leads nine of fourteen rows. Two things are worth extracting
-anyway. First, TwIL-LM3 improves on its own base while sitting mid-table (0.7339 against 0.7193
 on the 10-dataset macro), which is the point of the WiSE-FT stage — in-domain gains without
 transfer collapse. Second, it produces the shortest generations of any arm here at 482 tokens
 and consequently the most answers per second at 32.9, roughly eight times the 120B's rate.

 | mean gen length | **564** | 999 | 696 | 2296 | 1830 |
 | **ans/s** | **28.1** | 15.6 | 23.2 | 10.9 | 12.0 |
 `strict-7` is the mean of seven lanes scored under strict metrics only (`fol_translation`,
 `entailment_label`, `mcq_answer`, `semantic_parse` and `lean_formalize` exact match,
 ratio rather than re-tokenized directly; the method reproduces the three directly measured
 lengths to within 3.5%.
+First, TwIL-LM3 improves on its own base while sitting mid-table (0.7339 against 0.7193
 on the 10-dataset macro), which is the point of the WiSE-FT stage — in-domain gains without
 transfer collapse. Second, it produces the shortest generations of any arm here at 482 tokens
 and consequently the most answers per second at 32.9, roughly eight times the 120B's rate.