Instructions to use wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64 with Transformers:

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

pipe = pipeline("text-generation", model="wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64")
model = AutoModelForCausalLM.from_pretrained("wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64 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 wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64
# Run inference directly in the terminal:
llama cli -hf wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64
# Run inference directly in the terminal:
llama cli -hf wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64

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 wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64
# Run inference directly in the terminal:
./llama-cli -hf wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64

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 wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64

Use Docker

docker model run hf.co/wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64

LM Studio
Jan

vLLM

How to use wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64

SGLang

How to use wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64 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 "wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64 with Ollama:
```
ollama run hf.co/wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64
```

Unsloth Studio

How to use wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64 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 wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64 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 wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64 to start chatting

Docker Model Runner
How to use wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64 with Docker Model Runner:
```
docker model run hf.co/wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64
```

Lemonade

How to use wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull wizzl0r/scryptonaut-codellama-instruct-13b-alpaca-lora64

Run and chat with the model

lemonade run user.scryptonaut-codellama-instruct-13b-alpaca-lora64-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

scryptonaut-codellama-instruct-13b-alpaca-lora64

Commit History

initial

6b125ea

wizzl0r commited on Sep 27, 2023

initial commit

94500da

wizzl0r commited on Sep 27, 2023

Commit History

initial 6b125ea

initial commit 94500da

initial

6b125ea

initial commit

94500da