Instructions to use igidn/SmolLM3-Chat-v1-Adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use igidn/SmolLM3-Chat-v1-Adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM3-3B-Base") model = PeftModel.from_pretrained(base_model, "igidn/SmolLM3-Chat-v1-Adapter") - Transformers
How to use igidn/SmolLM3-Chat-v1-Adapter with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("igidn/SmolLM3-Chat-v1-Adapter", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from igidn/SmolLM3-Chat-v1-Adapter: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/igidn/SmolLM3-Chat-v1-Adapter/resolve/main/tokenizer.json
- Command line
-
hf download hf://igidn/SmolLM3-Chat-v1-Adapter/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/igidn/SmolLM3-Chat-v1-Adapter/resolve/main/tokenizer.json
17.2 MB
- Xet hash:
- bf85772905c05071eac8b295df5a86031e37cf207d570a3ad27e99dcff4e62b0
- Size of remote file:
- 17.2 MB
- SHA256:
- ab4da6b2aa68247e9c0fa9b97fc7fcc796505038d01f7e144522a65ce0dbd2e5
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