Instructions to use snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF",
	filename="Seed-Coder-8B-Instruct.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF:Q4_K_M

Use Docker

docker model run hf.co/snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF with Ollama:
```
ollama run hf.co/snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF:Q4_K_M
```

Unsloth Studio

How to use snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF 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-GGUF 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-GGUF 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-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF with Docker Model Runner:
```
docker model run hf.co/snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF:Q4_K_M
```

Lemonade

How to use snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Seed-Coder-8B-Instruct-Rust-Strandset-GGUF-Q4_K_M

List all available models

lemonade list

Seed-Coder-8B-Instruct-Rust-Strandset-GGUF / README.md

snsnc

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	---
	license: apache-2.0
	base_model: ByteDance-Seed/Seed-Coder-8B-Instruct
	tags:
	- gguf
	- llama.cpp
	- seed-coder
	- rust
	- strandset
	- code
	- q4_k_m
	- q6_k
	---

	# Seed-Coder-8B-Instruct Rust Strandset GGUF

	GGUF export of a Seed-Coder-8B-Instruct LoRA trained on a 20k-row subset of `Fortytwo-Network/Strandset-Rust-v1`.

	Base model:

	`ByteDance-Seed/Seed-Coder-8B-Instruct`

	LoRA adapter:

	`snsnc/Seed-Coder-8B-Instruct-Rust-Strandset-LoRA`

	Dataset:

	`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

	Files:

	- `Seed-Coder-8B-Instruct.Q4_K_M.gguf`
	- `Seed-Coder-8B-Instruct.Q6_K.gguf`

	Note: this model was trained on Strandset-style structured Rust code tasks. It may emit JSON-style wrappers such as `{"code": "..."}` depending on prompting.

	## llama.cpp

	```bash
	llama-cli \
	-m Seed-Coder-8B-Instruct.Q4_K_M.gguf \
	--jinja \
	--single-turn \
	--temp 0.1 \
	--top-p 0.8 \
	--repeat-penalty 1.05 \
	-p "Write only Rust code. No markdown. Implement parse_duration(s: &str) -> Result<std::time::Duration, String> supporting 10s, 5m, 2h. Include tests."