Instructions to use LoneStriker/Yi-9B-200K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use LoneStriker/Yi-9B-200K-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 LoneStriker/Yi-9B-200K-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LoneStriker/Yi-9B-200K-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 LoneStriker/Yi-9B-200K-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf LoneStriker/Yi-9B-200K-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 LoneStriker/Yi-9B-200K-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LoneStriker/Yi-9B-200K-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 LoneStriker/Yi-9B-200K-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LoneStriker/Yi-9B-200K-GGUF:Q4_K_M

Use Docker

docker model run hf.co/LoneStriker/Yi-9B-200K-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use LoneStriker/Yi-9B-200K-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LoneStriker/Yi-9B-200K-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LoneStriker/Yi-9B-200K-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LoneStriker/Yi-9B-200K-GGUF:Q4_K_M

Ollama
How to use LoneStriker/Yi-9B-200K-GGUF with Ollama:
```
ollama run hf.co/LoneStriker/Yi-9B-200K-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use LoneStriker/Yi-9B-200K-GGUF with Docker Model Runner:
```
docker model run hf.co/LoneStriker/Yi-9B-200K-GGUF:Q4_K_M
```

Lemonade

How to use LoneStriker/Yi-9B-200K-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LoneStriker/Yi-9B-200K-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Yi-9B-200K-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Update README.md with license information

by Chen-01AI - opened Jun 25, 2024

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+23

-11

Chen-01AI

Jun 25, 2024

•

edited Jun 27, 2024

Today I'm sending you the PR to help you update the model license, and give a recommendation according to apache-2.0.

License Update:
Since license of all Yi Series models has been updated from yi-license to apache-2.0, this PR is to help you update it.
License under apache-2.0 enables more free and flexible use and distribution, promoting open collaboration and innovation. It can be a good choice to make your models widely available and provide access which is reliable and high-quality. (https://www.apache.org/licenses/LICENSE-2.0)
If it looks good to you, you can choose to update other yi derivatives (if you have) license to apache-2.0 on your own if I miss out.
Recommendation for Yi Derivatives:
All Yi Series models are now licensed under apache-2.0. It is recomended that Yi derivatives mention the specific Yi models they're based on in any place (e.g., in the Model Card) to align with the requirement of apache-2.0.
Ways of changing other derivatives:
I've noticed that you have created many inspiring Yi derivatives.
Since each pull request (PR) sends you a notification, I'm concerned that sending PRs for all your derivatives might overwhelm you with emails.
To avoid this, I’m wondering which approach you’d prefer: should I send PRs for each derivative, or would it be better for you to update them on your own? Please let me know your decision, thanks!
Thanks for your continued support and contributions to Yi models.

Update README.md with license information0cf0d349

Chen-01AI

Jul 5, 2024

Hi, have you reviewd this PR? If it looks good to you, you can merge it! 😀 And tell me the way that you would like to use to update other derivatives now licensed under yi-license.

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