Instructions to use tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-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 tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF:Q2_K

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama cli -hf tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF:Q2_K

Llama new
LM Studio
Jan
Ollama
How to use tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF with Ollama:
```
ollama run hf.co/tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF:Q2_K
```
Unsloth Desktop
Docker Model Runner
How to use tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF:Q2_K
```

Lemonade

How to use tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF:Q2_K

Run and chat with the model

lemonade run user.willyli_Seed-Coder-8B-Instruct-KTO-GGUF-Q2_K

List all available models

lemonade list

Atomic Chat

willyli/Seed-Coder-8B-Instruct-KTO - GGUF

Join our Discord to learn more about what we're building ↗

This repo contains GGUF format model files for willyli/Seed-Coder-8B-Instruct-KTO.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5753.

Our projects

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Prompt template

<[begin▁of▁sentence]>system
{system_prompt}<[end▁of▁sentence]><[begin▁of▁sentence]>user
{prompt}<[end▁of▁sentence]><[begin▁of▁sentence]>assistant

Model file specification

Filename	Quant type	File Size	Description
Seed-Coder-8B-Instruct-KTO-Q2_K.gguf	Q2_K	3.304 GB	smallest, significant quality loss - not recommended for most purposes
Seed-Coder-8B-Instruct-KTO-Q3_K_S.gguf	Q3_K_S	3.801 GB	very small, high quality loss
Seed-Coder-8B-Instruct-KTO-Q3_K_M.gguf	Q3_K_M	4.155 GB	very small, high quality loss
Seed-Coder-8B-Instruct-KTO-Q3_K_L.gguf	Q3_K_L	4.458 GB	small, substantial quality loss
Seed-Coder-8B-Instruct-KTO-Q4_0.gguf	Q4_0	4.812 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Seed-Coder-8B-Instruct-KTO-Q4_K_S.gguf	Q4_K_S	4.843 GB	small, greater quality loss
Seed-Coder-8B-Instruct-KTO-Q4_K_M.gguf	Q4_K_M	5.071 GB	medium, balanced quality - recommended
Seed-Coder-8B-Instruct-KTO-Q5_0.gguf	Q5_0	5.764 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Seed-Coder-8B-Instruct-KTO-Q5_K_S.gguf	Q5_K_S	5.764 GB	large, low quality loss - recommended
Seed-Coder-8B-Instruct-KTO-Q5_K_M.gguf	Q5_K_M	5.897 GB	large, very low quality loss - recommended
Seed-Coder-8B-Instruct-KTO-Q6_K.gguf	Q6_K	6.775 GB	very large, extremely low quality loss
Seed-Coder-8B-Instruct-KTO-Q8_0.gguf	Q8_0	8.773 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF --include "Seed-Coder-8B-Instruct-KTO-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'

Downloads last month: 31

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for tensorblock/willyli_Seed-Coder-8B-Instruct-KTO-GGUF

Base model

ByteDance-Seed/Seed-Coder-8B-Base

Finetuned

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

Finetuned

willyli/Seed-Coder-8B-Instruct-KTO

Quantized

(2)

this model