Instructions to use Sido/makellm-ja-char-75m-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Sido/makellm-ja-char-75m-chat 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 Sido/makellm-ja-char-75m-chat:F16
# Run inference directly in the terminal:
llama cli -hf Sido/makellm-ja-char-75m-chat:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Sido/makellm-ja-char-75m-chat:F16
# Run inference directly in the terminal:
llama cli -hf Sido/makellm-ja-char-75m-chat:F16

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 Sido/makellm-ja-char-75m-chat:F16
# Run inference directly in the terminal:
./llama-cli -hf Sido/makellm-ja-char-75m-chat:F16

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 Sido/makellm-ja-char-75m-chat:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Sido/makellm-ja-char-75m-chat:F16

Use Docker

docker model run hf.co/Sido/makellm-ja-char-75m-chat:F16

LM Studio
Jan

vLLM

How to use Sido/makellm-ja-char-75m-chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sido/makellm-ja-char-75m-chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sido/makellm-ja-char-75m-chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sido/makellm-ja-char-75m-chat:F16

Ollama
How to use Sido/makellm-ja-char-75m-chat with Ollama:
```
ollama run hf.co/Sido/makellm-ja-char-75m-chat:F16
```
Unsloth Desktop
Docker Model Runner
How to use Sido/makellm-ja-char-75m-chat with Docker Model Runner:
```
docker model run hf.co/Sido/makellm-ja-char-75m-chat:F16
```

Lemonade

How to use Sido/makellm-ja-char-75m-chat with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Sido/makellm-ja-char-75m-chat:F16

Run and chat with the model

lemonade run user.makellm-ja-char-75m-chat-F16

List all available models

lemonade list

Atomic Chat

makellm-ja-char-75m-chat

File size: 821 Bytes

376a4f4

FROM ./makellm-ja-char-75m-chat.f16.gguf

TEMPLATE """{{ range .Messages }}{{ if eq .Role "user" }}<|user|>{{ .Content }}<|bot|>{{ else if eq .Role "assistant" }}{{ .Content }}<|end|>{{ end }}{{ end }}"""

PARAMETER stop "<|end|>"
PARAMETER stop "<|user|>"
PARAMETER num_ctx 256
PARAMETER temperature 0.7
PARAMETER top_k 40
PARAMETER repeat_penalty 1.1

LICENSE """
makellm-ja-char-75m-chat is released under CC-BY-SA-4.0.
https://creativecommons.org/licenses/by-sa/4.0/

Training data attribution:
- CC-100 Japanese (range3/cc100-ja; CommonCrawl, cite XLM-R & CCNet)
- Japanese Wikipedia (wikimedia/wikipedia; CC-BY-SA)
- Aozora Bunko (globis-university/aozorabunko-clean; public domain)
- oasst1-89k-ja (kunishou/oasst1-89k-ja; Apache-2.0)
- databricks-dolly-15k-ja (kunishou/databricks-dolly-15k-ja; CC-BY-SA-3.0)
"""