Text Generation
Transformers
Safetensors
GGUF
English
llama
formal-logic
reasoning
lora
model-merging
wise-ft
reinforcement-learning
grpo
smollm2
twil-lm
conversational
text-generation-inference
Instructions to use webAI-Official/TwIL-LM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use webAI-Official/TwIL-LM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="webAI-Official/TwIL-LM") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("webAI-Official/TwIL-LM") model = AutoModelForCausalLM.from_pretrained("webAI-Official/TwIL-LM", 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-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
- SGLang
How to use webAI-Official/TwIL-LM 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-LM" \ --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-LM", "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-LM" \ --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-LM", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - 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
TwIL-LM2: v5 MGPO weights, normalised tokenizer metadata
#4
by anurag051194 - opened
- special_tokens_map.json +30 -0
- tokenizer_config.json +141 -4
special_tokens_map.json
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{
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"bos_token": {
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"content": "<|im_start|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "<|im_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<|im_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer_config.json
CHANGED
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": "<|im_start|>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"is_local": true,
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"model_max_length": 8192,
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"pad_token": "<|im_end|>",
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"tokenizer_class": "
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"unk_token": "<|endoftext|>",
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"vocab_size": 49152
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}
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{
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"add_prefix_space": false,
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"bos_token": "<|im_start|>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"model_max_length": 8192,
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"pad_token": "<|im_end|>",
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"tokenizer_class": "PreTrainedTokenizerFast",
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"unk_token": "<|endoftext|>",
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"vocab_size": 49152,
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"added_tokens_decoder": {
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"0": {
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"content": "<|endoftext|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "<|im_start|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "<|im_end|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"3": {
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"content": "<repo_name>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"4": {
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"content": "<reponame>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"5": {
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"content": "<file_sep>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"6": {
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"content": "<filename>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"7": {
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"content": "<gh_stars>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"8": {
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"content": "<issue_start>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"9": {
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"content": "<issue_comment>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"10": {
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"content": "<issue_closed>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"11": {
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"content": "<jupyter_start>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"12": {
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"content": "<jupyter_text>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"13": {
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"content": "<jupyter_code>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"14": {
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"content": "<jupyter_output>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"15": {
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"content": "<jupyter_script>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"16": {
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"content": "<empty_output>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
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}
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