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)

# pip install -U transformers accelerate
# 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=256)
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

Llama new
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"

Super lazy.

by BigDeeper - opened 1 day ago

Discussion

BigDeeper

1 day ago

•

edited 1 day ago

Am I doing something wrong? The model is basically refusing to do any work.

I pass a largish set of instructions via -sysf SYS_PROMPT.md to llama.cpp/build/bin/llama-cli, pass some text to work on at the interactive prompt, and

it is give me a few thoughts, which are paraphrases of the system prompt's instructions (a tiny fraction) and then stops.

I seem to have the opposite problem, using another variant of llama.cpp/build/bin/llama-server (my modified version with caching and multiple processing slots), which I was using very successfully with Qwen3.8-27B-GGUF, and it works for a while with somewhat poor adherence to the sys prompt, but it never stops, it just continues generating forever.

I am not certain what it is generating,, I have to look.

Can someone shed some light on 1) how to make it WORK, instead of stopping after 3-4 lines of thoughts (never closes this) and 2) how to reliably stop it?

With Qwen I was relying on the last } of the JSON object, but it does not seem to work with this model.

anurag051194

webAI org 1 day ago

Sorry that the model is not performing for your use case. Can you share the full stacktrace if possible. I think by default, llama cpp sets a smaller context window size which might be an issue. Here are a few recommendations from me

Try fixing the token allocations in llama-cli
llama-cli
-m TwIL-LM3-Pro-Q4_K_M.gguf
-c 16384
-n 4096
--temp 1.0
--top-p 0.95
--conversation
-sysf SYS_PROMPT.md
One more common issue since it uses a granite backend is that having the correct stop sequence which is different from qwen.
Stop strings: ["<|end_of_text|>", "<|start_of_role|>", "<|end_of_role|>"] , these are for the granite based models
Or else try the granite chat format
<|start_of_role|>system<|end_of_role|>
{Your system instructions here}<|end_of_text|>
<|start_of_role|>user<|end_of_role|>
{User prompt here}<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>
The last option would be just setting thinking off and then trying to create JSON if needed. I personally wont recommend this option but hope that the first three solutions work for you.

I can provide a better solution if I get the stacktrace or example to reproduce the error.

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