Instructions to use Sayan01/Llama-Flan-XL2base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sayan01/Llama-Flan-XL2base with Transformers:

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

pipe = pipeline("text-generation", model="Sayan01/Llama-Flan-XL2base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sayan01/Llama-Flan-XL2base")
model = AutoModelForCausalLM.from_pretrained("Sayan01/Llama-Flan-XL2base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Sayan01/Llama-Flan-XL2base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sayan01/Llama-Flan-XL2base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sayan01/Llama-Flan-XL2base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Sayan01/Llama-Flan-XL2base

SGLang

How to use Sayan01/Llama-Flan-XL2base 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 "Sayan01/Llama-Flan-XL2base" \
    --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": "Sayan01/Llama-Flan-XL2base",
		"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 "Sayan01/Llama-Flan-XL2base" \
        --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": "Sayan01/Llama-Flan-XL2base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Sayan01/Llama-Flan-XL2base with Docker Model Runner:
```
docker model run hf.co/Sayan01/Llama-Flan-XL2base
```

Llama-Flan-XL2base / README.md

Sayan01

Update README.md

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metadata

license: apache-2.0
language:
  - en
datasets:
  - Open-Orca/FLAN

This is a 230M parameter Small Llama model distilled from the Original one. The model is distilled on OpenOrca's FLAN dataset. The distillation ran over 160000 random samples of FLAN dataset. It is free to download. Also, it is a work in progress, so please use it at your own risk