Instructions to use snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-LoRA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("ByteDance-Seed/Seed-Coder-8B-Instruct") model = PeftModel.from_pretrained(base_model, "snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-LoRA") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-LoRA 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 snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-LoRA 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 snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-LoRA to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-LoRA to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-LoRA", max_seq_length=2048, )
- Xet hash:
- 7788495381c5276ba53487783bbbd8ee33a349c02d90bc4e28e40fb8470dfb95
- Size of remote file:
- 1.47 kB
- SHA256:
- 061b7a2dd3f7c788b29602e1b3cf3c93b69ca9e0f8770630e4838763876a45a0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.