Hypa-SmolLM
Collection
Multilingual SmolLM releases from Hypa Intelligence: lightweight models for keyboard tasks like autocorrect and prediction across 22 languages. • 3 items • Updated
How to use hypaai/Hypa-SmolLM-135M-Instruct-LoRAs with Transformers:
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("hypaai/Hypa-SmolLM-135M-Instruct-LoRAs", device_map="auto")How to use hypaai/Hypa-SmolLM-135M-Instruct-LoRAs with Unsloth Studio:
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 hypaai/Hypa-SmolLM-135M-Instruct-LoRAs to start chatting
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 hypaai/Hypa-SmolLM-135M-Instruct-LoRAs to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for hypaai/Hypa-SmolLM-135M-Instruct-LoRAs to start chatting
pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
model_name="hypaai/Hypa-SmolLM-135M-Instruct-LoRAs",
max_seq_length=2048,
)This model is a fine-tuned version of unsloth/smollm-135m-instruct-bnb-4bit. It has been trained using TRL.
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="hypaai/Hypa-SmolLM-135M-Instruct-SFT-runpod-2026-07-28_LoRAs", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
This model was trained with SFT.
Cite TRL as:
@software{vonwerra2020trl,
title = {{TRL: Transformers Reinforcement Learning}},
author = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
license = {Apache-2.0},
url = {https://github.com/huggingface/trl},
year = {2020}
}