Instructions to use Aakash098/ShinchanAI-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Aakash098/ShinchanAI-small 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 Aakash098/ShinchanAI-small:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Aakash098/ShinchanAI-small:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Aakash098/ShinchanAI-small:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Aakash098/ShinchanAI-small: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 Aakash098/ShinchanAI-small:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Aakash098/ShinchanAI-small: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 Aakash098/ShinchanAI-small:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Aakash098/ShinchanAI-small:Q4_K_M

Use Docker

docker model run hf.co/Aakash098/ShinchanAI-small:Q4_K_M

LM Studio
Jan

vLLM

How to use Aakash098/ShinchanAI-small with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Aakash098/ShinchanAI-small"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aakash098/ShinchanAI-small",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Aakash098/ShinchanAI-small:Q4_K_M

Ollama
How to use Aakash098/ShinchanAI-small with Ollama:
```
ollama run hf.co/Aakash098/ShinchanAI-small:Q4_K_M
```

Unsloth Studio

How to use Aakash098/ShinchanAI-small 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 Aakash098/ShinchanAI-small 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 Aakash098/ShinchanAI-small to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Aakash098/ShinchanAI-small to start chatting

Docker Model Runner
How to use Aakash098/ShinchanAI-small with Docker Model Runner:
```
docker model run hf.co/Aakash098/ShinchanAI-small:Q4_K_M
```

Lemonade

How to use Aakash098/ShinchanAI-small with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Aakash098/ShinchanAI-small:Q4_K_M

Run and chat with the model

lemonade run user.ShinchanAI-small-Q4_K_M

List all available models

lemonade list

Atomic Chat

Aakash098 commited on Mar 8

Commit

2c925b5

verified ·

1 Parent(s): bbdf52b

Update README.md

Browse files

Files changed (1) hide show

README.md +9 -13

README.md CHANGED Viewed

@@ -1,8 +1,3 @@
-code
-Markdown
-download
-content_copy
-expand_less
 ---
 language:
   - en
@@ -86,12 +81,11 @@ To get the best out of this model and ensure it stays perfectly in character, yo
 ```bash
 llama-cli -hf Aakash098/ShinchanAI-small --jinja -p "System: Tu Shinchan hai... [Paste Prompt] \nUser: Hello Shinchan!\nShinchan: "
-Python (llama-cpp-python)
-code
-Python
-download
-content_copy
-expand_less
 from llama_cpp import Llama
 llm = Llama.from_pretrained(
@@ -108,9 +102,11 @@ response = llm.create_chat_completion(
 )
 print(response["choices"][0]["message"]["content"])
-💖 Acknowledgements
 This model was trained with the incredible tools provided by Unsloth.
 <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>
-```

 ---
 language:
   - en
 ```bash
 llama-cli -hf Aakash098/ShinchanAI-small --jinja -p "System: Tu Shinchan hai... [Paste Prompt] \nUser: Hello Shinchan!\nShinchan: "
+```
+### Python (llama-cpp-python)
+```python
 from llama_cpp import Llama
 llm = Llama.from_pretrained(
 )
 print(response["choices"][0]["message"]["content"])
+```
+## 💖 Acknowledgements
 This model was trained with the incredible tools provided by Unsloth.
 <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>
+```