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

Libraries
PEFT
How to use webAI-Official/TwIL-LM with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

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

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "webAI-Official/TwIL-LM"
# 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-LM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

lemonade run user.TwIL-LM-Q4_K_M

List all available models

lemonade list

Atomic Chat

cutterdalton-webai

anurag051194 commited on about 23 hours ago

Commit

656c0a3

1 Parent(s): e309060

Update README.md (#2)

Browse files

- Update README.md (85bc9963e9e353740a6c5b5a482f730ec559762c)

Co-authored-by: Anurag Daram <anurag051194@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +2 -1

README.md CHANGED Viewed

@@ -24,7 +24,8 @@ TwiL-LM(1.7B) is a parameter-efficient LoRA adapter for
 [SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct).
 It is designed for formal-logic tasks including first-order logic (FOL)
 translation, entailment classification, multiple-choice reasoning, semantic
-parsing, Lean assistance, and procedural reasoning.
 ## Model details

 [SmolLM2-1.7B-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct).
 It is designed for formal-logic tasks including first-order logic (FOL)
 translation, entailment classification, multiple-choice reasoning, semantic
+parsing, Lean assistance, and procedural reasoning. Our updated TwIL-LM2 model can be downloaded from
+the TwIL-LM2 branch. That model shows better general reasoning capabilities while solving formal logic tasks.
 ## Model details