Instructions to use prompt-agnostic-language-models/Llama-1B_ties with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prompt-agnostic-language-models/Llama-1B_ties with Transformers:

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

pipe = pipeline("text-generation", model="prompt-agnostic-language-models/Llama-1B_ties")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prompt-agnostic-language-models/Llama-1B_ties")
model = AutoModelForCausalLM.from_pretrained("prompt-agnostic-language-models/Llama-1B_ties", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use prompt-agnostic-language-models/Llama-1B_ties with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prompt-agnostic-language-models/Llama-1B_ties"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prompt-agnostic-language-models/Llama-1B_ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/prompt-agnostic-language-models/Llama-1B_ties

SGLang

How to use prompt-agnostic-language-models/Llama-1B_ties 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 "prompt-agnostic-language-models/Llama-1B_ties" \
    --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": "prompt-agnostic-language-models/Llama-1B_ties",
		"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 "prompt-agnostic-language-models/Llama-1B_ties" \
        --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": "prompt-agnostic-language-models/Llama-1B_ties",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use prompt-agnostic-language-models/Llama-1B_ties with Docker Model Runner:
```
docker model run hf.co/prompt-agnostic-language-models/Llama-1B_ties
```

Llama-1B_ties / model.safetensors

frederic-sadrieh

Upload folder using huggingface_hub

ec87f65 verified 3 months ago

Download

History Blame Contribute Delete

2.47 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: b0a6b548284340e040f475c9c98f325906ab1ad6581737226a162497ddb61a89
Size of remote file:: 2.47 GB
SHA256:: 62c23b34431af719319a62a45b41f40446bf7a635dfbfee8938eb8d80ebc72e5

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.