Text Generation
Transformers
TensorBoard
Safetensors
llama
Generated from Trainer
smol-course
module_1
trl
sft
conversational
text-generation-inference
Instructions to use PhilSad/SmolLM2-135M-FT-SCP-Wiki with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PhilSad/SmolLM2-135M-FT-SCP-Wiki with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PhilSad/SmolLM2-135M-FT-SCP-Wiki") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("PhilSad/SmolLM2-135M-FT-SCP-Wiki") model = AutoModelForCausalLM.from_pretrained("PhilSad/SmolLM2-135M-FT-SCP-Wiki") 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
- vLLM
How to use PhilSad/SmolLM2-135M-FT-SCP-Wiki with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "PhilSad/SmolLM2-135M-FT-SCP-Wiki" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "PhilSad/SmolLM2-135M-FT-SCP-Wiki", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/PhilSad/SmolLM2-135M-FT-SCP-Wiki
- SGLang
How to use PhilSad/SmolLM2-135M-FT-SCP-Wiki 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 "PhilSad/SmolLM2-135M-FT-SCP-Wiki" \ --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": "PhilSad/SmolLM2-135M-FT-SCP-Wiki", "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 "PhilSad/SmolLM2-135M-FT-SCP-Wiki" \ --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": "PhilSad/SmolLM2-135M-FT-SCP-Wiki", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use PhilSad/SmolLM2-135M-FT-SCP-Wiki with Docker Model Runner:
docker model run hf.co/PhilSad/SmolLM2-135M-FT-SCP-Wiki
Model Card for SmolLM2-135M-FT-SCP-Wiki
This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M. It has been trained using TRL.
Quick start
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "PhilSad/SmolLM2-1.7B-FT-SCP-Wiki"
model = AutoModelForCausalLM.from_pretrained(
pretrained_model_name_or_path=model_name
).to(device)
tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path=model_name)
prompt = "SCP-10214 is a god who loves making pasta."
messages = [{"role": "user", "content": prompt}]
formatted_prompt = tokenizer.apply_chat_template(messages, tokenize=False)
inputs = tokenizer(formatted_prompt, return_tensors="pt").to(device)
outputs = model.generate(**inputs, max_new_tokens=2048)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training procedure
This model was trained with SFT.
Framework versions
- TRL: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.5.1+cu121
- Datasets: 3.2.0
- Tokenizers: 0.20.3
Citations
Cite TRL as:
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
- Downloads last month
- 4
Model tree for PhilSad/SmolLM2-135M-FT-SCP-Wiki
Base model
HuggingFaceTB/SmolLM2-135M