Instructions to use Navpy/phi-3.5-AI-Vtuber-json with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Navpy/phi-3.5-AI-Vtuber-json with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Navpy/phi-3.5-AI-Vtuber-json",
	filename="v1-phi-3.5-mini-instruct.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Navpy/phi-3.5-AI-Vtuber-json with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Navpy/phi-3.5-AI-Vtuber-json:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Navpy/phi-3.5-AI-Vtuber-json:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Navpy/phi-3.5-AI-Vtuber-json:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Navpy/phi-3.5-AI-Vtuber-json: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 Navpy/phi-3.5-AI-Vtuber-json:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Navpy/phi-3.5-AI-Vtuber-json: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 Navpy/phi-3.5-AI-Vtuber-json:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Navpy/phi-3.5-AI-Vtuber-json:Q4_K_M

Use Docker

docker model run hf.co/Navpy/phi-3.5-AI-Vtuber-json:Q4_K_M

LM Studio
Jan

vLLM

How to use Navpy/phi-3.5-AI-Vtuber-json with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Navpy/phi-3.5-AI-Vtuber-json"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Navpy/phi-3.5-AI-Vtuber-json",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Navpy/phi-3.5-AI-Vtuber-json:Q4_K_M

Ollama
How to use Navpy/phi-3.5-AI-Vtuber-json with Ollama:
```
ollama run hf.co/Navpy/phi-3.5-AI-Vtuber-json:Q4_K_M
```

Unsloth Studio

How to use Navpy/phi-3.5-AI-Vtuber-json 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 Navpy/phi-3.5-AI-Vtuber-json 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 Navpy/phi-3.5-AI-Vtuber-json to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Navpy/phi-3.5-AI-Vtuber-json to start chatting

Docker Model Runner
How to use Navpy/phi-3.5-AI-Vtuber-json with Docker Model Runner:
```
docker model run hf.co/Navpy/phi-3.5-AI-Vtuber-json:Q4_K_M
```

Lemonade

How to use Navpy/phi-3.5-AI-Vtuber-json with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Navpy/phi-3.5-AI-Vtuber-json:Q4_K_M

Run and chat with the model

lemonade run user.phi-3.5-AI-Vtuber-json-Q4_K_M

List all available models

lemonade list

Navpy commited on Mar 12

Commit

53bc714

verified ·

1 Parent(s): d6f3eb6

Update README.md

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README.md CHANGED Viewed

@@ -14,7 +14,7 @@ pipeline_tag: text-generation
 # Version 2.0: The "Native JSON" Update
 The model has been completely re-trained from the ground up to be more stable, expressive, and structurally sound.
 ## What's New in V2?
-While V1 was successfully fine-tuned to repond in JSON format, it required a System Prompt to guide the model for that behavior. V2 has been deeply trained (455 steps, 0.39 loss) to make JSON and the personality its native language. It now understands the JSON structure at a foundational level, making it more 'alive,' talkative, and responsive even with minimal prompting.
 If you are downloading the new version, you can still use your old Modelfile! However, you will notice that responses feels much more talkative and emotional because the model is deeply fine-tuned.
 - For Ollama users A Modelfile is added for you to get the best results out of the model(RECOMMENDED).

 # Version 2.0: The "Native JSON" Update
 The model has been completely re-trained from the ground up to be more stable, expressive, and structurally sound.
 ## What's New in V2?
+While V1 was successfully fine-tuned to repond in JSON format, it required a System Prompt to guide the model for that behavior. V2 has been deeply trained 2epochs (0.62 loss) to make JSON and the personality its native language. It now understands the JSON structure at a foundational level, making it more 'alive,' talkative, and responsive even with minimal prompting.
 If you are downloading the new version, you can still use your old Modelfile! However, you will notice that responses feels much more talkative and emotional because the model is deeply fine-tuned.
 - For Ollama users A Modelfile is added for you to get the best results out of the model(RECOMMENDED).