How to use from
Unsloth StudioInstall 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 chattingUsing 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 chattingLoad 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,
)Quick Links
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→ useroutput_data→ assistanttask_category→ systemcrate_name→ systemtest→ 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.
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Model tree for snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-LoRA
Base model
ByteDance-Seed/Seed-Coder-8B-Base Finetuned
ByteDance-Seed/Seed-Coder-8B-Instruct
Install Unsloth Studio (macOS, Linux, WSL)
# 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