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, )
| license: apache-2.0 | |
| base_model: ByteDance-Seed/Seed-Coder-8B-Instruct | |
| library_name: peft | |
| tags: | |
| - lora | |
| - unsloth | |
| - seed-coder | |
| - rust | |
| - strandset | |
| - code | |
| # Seed-Coder-8B-Instruct Rust Strandset LoRA | |
| LoRA adapter trained with Unsloth Studio on a 20k-row subset of `Fortytwo-Network/Strandset-Rust-v1`. | |
| Base model: | |
| `ByteDance-Seed/Seed-Coder-8B-Instruct` | |
| Training data: | |
| `Fortytwo-Network/Strandset-Rust-v1` | |
| Mapping used: | |
| - `input_data` → user | |
| - `output_data` → assistant | |
| - `task_category` → system | |
| - `crate_name` → system | |
| - `test` → none | |
| Training config: | |
| - Method: LoRA | |
| - Context: 4096 | |
| - Epochs: 1 | |
| - LR: 2e-4 | |
| - Rank: 16 | |
| - Alpha: 32 | |
| - Dropout: 0.0 | |
| - Batch size: 16 | |
| - Grad accum: 2 | |
| - Effective batch: 32 | |
| - Weight decay: 0.001 | |
| - Warmup steps: 25 | |
| - Packing: off | |
| - Train on completions: on | |
| Note: this adapter was trained on Strandset-style structured Rust code tasks. It may emit JSON-style wrappers such as `{"code": "..."}` depending on prompting. | |