nsfw

Instructions to use D1rtyB1rd/Dirty-Alice with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use D1rtyB1rd/Dirty-Alice with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="D1rtyB1rd/Dirty-Alice")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("D1rtyB1rd/Dirty-Alice")
model = AutoModelForCausalLM.from_pretrained("D1rtyB1rd/Dirty-Alice")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use D1rtyB1rd/Dirty-Alice with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="D1rtyB1rd/Dirty-Alice",
	filename="Dirty-Alice-fp16.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 D1rtyB1rd/Dirty-Alice 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 D1rtyB1rd/Dirty-Alice
# Run inference directly in the terminal:
llama cli -hf D1rtyB1rd/Dirty-Alice

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf D1rtyB1rd/Dirty-Alice
# Run inference directly in the terminal:
llama cli -hf D1rtyB1rd/Dirty-Alice

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 D1rtyB1rd/Dirty-Alice
# Run inference directly in the terminal:
./llama-cli -hf D1rtyB1rd/Dirty-Alice

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 D1rtyB1rd/Dirty-Alice
# Run inference directly in the terminal:
./build/bin/llama-cli -hf D1rtyB1rd/Dirty-Alice

Use Docker

docker model run hf.co/D1rtyB1rd/Dirty-Alice

LM Studio
Jan

vLLM

How to use D1rtyB1rd/Dirty-Alice with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "D1rtyB1rd/Dirty-Alice"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "D1rtyB1rd/Dirty-Alice",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/D1rtyB1rd/Dirty-Alice

SGLang

How to use D1rtyB1rd/Dirty-Alice with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "D1rtyB1rd/Dirty-Alice" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "D1rtyB1rd/Dirty-Alice",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "D1rtyB1rd/Dirty-Alice" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "D1rtyB1rd/Dirty-Alice",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use D1rtyB1rd/Dirty-Alice with Ollama:
```
ollama run hf.co/D1rtyB1rd/Dirty-Alice
```

Unsloth Studio

How to use D1rtyB1rd/Dirty-Alice 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 D1rtyB1rd/Dirty-Alice 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 D1rtyB1rd/Dirty-Alice to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for D1rtyB1rd/Dirty-Alice to start chatting

Atomic Chat new
Docker Model Runner
How to use D1rtyB1rd/Dirty-Alice with Docker Model Runner:
```
docker model run hf.co/D1rtyB1rd/Dirty-Alice
```

Lemonade

How to use D1rtyB1rd/Dirty-Alice with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull D1rtyB1rd/Dirty-Alice

Run and chat with the model

lemonade run user.Dirty-Alice-{{QUANT_TAG}}

List all available models

lemonade list

D1rtyB1rd commited on Jun 3, 2024

Commit

7273db8

verified ·

1 Parent(s): 8d57861

Upload 9 files

Browse files

Files changed (10) hide show

.gitattributes +1 -0
Dirty-Alice-fp16.gguf +3 -0
config.json +32 -0
generation_config.json +11 -0
merges.txt +0 -0
model.safetensors +3 -0
special_tokens_map.json +20 -0
tokenizer.json +0 -0
tokenizer_config.json +59 -0
vocab.json +0 -0

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