Instructions to use TeeZee/llama-2-7B-pirate-speech-600s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TeeZee/llama-2-7B-pirate-speech-600s with Transformers:

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

pipe = pipeline("text-generation", model="TeeZee/llama-2-7B-pirate-speech-600s")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("TeeZee/llama-2-7B-pirate-speech-600s")
model = AutoModelForMultimodalLM.from_pretrained("TeeZee/llama-2-7B-pirate-speech-600s")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TeeZee/llama-2-7B-pirate-speech-600s with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TeeZee/llama-2-7B-pirate-speech-600s"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TeeZee/llama-2-7B-pirate-speech-600s",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TeeZee/llama-2-7B-pirate-speech-600s

SGLang

How to use TeeZee/llama-2-7B-pirate-speech-600s 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 "TeeZee/llama-2-7B-pirate-speech-600s" \
    --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": "TeeZee/llama-2-7B-pirate-speech-600s",
		"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 "TeeZee/llama-2-7B-pirate-speech-600s" \
        --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": "TeeZee/llama-2-7B-pirate-speech-600s",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio

How to use TeeZee/llama-2-7B-pirate-speech-600s 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 TeeZee/llama-2-7B-pirate-speech-600s 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 TeeZee/llama-2-7B-pirate-speech-600s to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TeeZee/llama-2-7B-pirate-speech-600s to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="TeeZee/llama-2-7B-pirate-speech-600s",
    max_seq_length=2048,
)

Docker Model Runner
How to use TeeZee/llama-2-7B-pirate-speech-600s with Docker Model Runner:
```
docker model run hf.co/TeeZee/llama-2-7B-pirate-speech-600s
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

After just 600 steps (80 minutes of training on free colab) effects are amazing:

Below is an instruction that describes a task. Write a response that appropriately completes the request.

explain what is machine learinig
### Response:
Machine learning be th' study of computer algorithms to mimic th' way humans learn and make decisions.
Machine learning uses data with known outcomes (called "labeled" data) to improve its performance on new, unseen data ("unlabeled" data).
Thar she blows! Blow me down!