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, )
File size: 969 Bytes
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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.
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