Instructions to use umiyuki/Japanese-WizardLM2-ChatV-7B-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 umiyuki/Japanese-WizardLM2-ChatV-7B-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 umiyuki/Japanese-WizardLM2-ChatV-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf umiyuki/Japanese-WizardLM2-ChatV-7B-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 umiyuki/Japanese-WizardLM2-ChatV-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf umiyuki/Japanese-WizardLM2-ChatV-7B-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 umiyuki/Japanese-WizardLM2-ChatV-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf umiyuki/Japanese-WizardLM2-ChatV-7B-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 umiyuki/Japanese-WizardLM2-ChatV-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf umiyuki/Japanese-WizardLM2-ChatV-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/umiyuki/Japanese-WizardLM2-ChatV-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use umiyuki/Japanese-WizardLM2-ChatV-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "umiyuki/Japanese-WizardLM2-ChatV-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "umiyuki/Japanese-WizardLM2-ChatV-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/umiyuki/Japanese-WizardLM2-ChatV-7B-GGUF:Q4_K_M

Ollama
How to use umiyuki/Japanese-WizardLM2-ChatV-7B-GGUF with Ollama:
```
ollama run hf.co/umiyuki/Japanese-WizardLM2-ChatV-7B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use umiyuki/Japanese-WizardLM2-ChatV-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/umiyuki/Japanese-WizardLM2-ChatV-7B-GGUF:Q4_K_M
```

Lemonade

How to use umiyuki/Japanese-WizardLM2-ChatV-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull umiyuki/Japanese-WizardLM2-ChatV-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Japanese-WizardLM2-ChatV-7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

umiyuki commited on Apr 17, 2024

Commit

ec1f3cd

verified ·

1 Parent(s): 8672a04

Update README.md

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README.md +1 -1

README.md CHANGED Viewed

@@ -20,7 +20,7 @@ ChatNTQの日本語能力にWizardLM-2の性能の高さが加わる事を狙い
 <table>
  <tr>
-   <th>Model<br>(Q8_0 quant)</th>
    <th><a href="https://huggingface.co/CohereForAI/c4ai-command-r-plus">c4ai-command-r-plus(Cohere API)</a></th>
    <th><a href="https://huggingface.co/TFMC/Japanese-Starling-ChatV-7B-GGUF">JA-Starling-ChatV-7B-GGUF(fp16)</th>
    <th>JA-WizardLM2-ChatV-7B-GGUF (This model)(fp16)</th>

 <table>
  <tr>
+   <th>Model</th>
    <th><a href="https://huggingface.co/CohereForAI/c4ai-command-r-plus">c4ai-command-r-plus(Cohere API)</a></th>
    <th><a href="https://huggingface.co/TFMC/Japanese-Starling-ChatV-7B-GGUF">JA-Starling-ChatV-7B-GGUF(fp16)</th>
    <th>JA-WizardLM2-ChatV-7B-GGUF (This model)(fp16)</th>