Instructions to use foss22/pruned7L-danube2-1.8b-base-17L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use foss22/pruned7L-danube2-1.8b-base-17L with Transformers:

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

pipe = pipeline("text-generation", model="foss22/pruned7L-danube2-1.8b-base-17L")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("foss22/pruned7L-danube2-1.8b-base-17L")
model = AutoModelForCausalLM.from_pretrained("foss22/pruned7L-danube2-1.8b-base-17L", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use foss22/pruned7L-danube2-1.8b-base-17L with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "foss22/pruned7L-danube2-1.8b-base-17L"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "foss22/pruned7L-danube2-1.8b-base-17L",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/foss22/pruned7L-danube2-1.8b-base-17L

SGLang

How to use foss22/pruned7L-danube2-1.8b-base-17L 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 "foss22/pruned7L-danube2-1.8b-base-17L" \
    --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": "foss22/pruned7L-danube2-1.8b-base-17L",
		"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 "foss22/pruned7L-danube2-1.8b-base-17L" \
        --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": "foss22/pruned7L-danube2-1.8b-base-17L",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use foss22/pruned7L-danube2-1.8b-base-17L with Docker Model Runner:
```
docker model run hf.co/foss22/pruned7L-danube2-1.8b-base-17L
```

pruned7L-danube2-1.8b-base-17L / model-00003-of-00003.safetensors

foss22

Upload MistralForCausalLM

31b25ee verified about 1 month ago

Download

History Blame Contribute Delete

698 MB

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:: a0eb5617fc4716539e852e2b1a09b394ba24aaf1c56f7a33d14d7d511ed185e9
Size of remote file:: 698 MB
SHA256:: 03e472df9ed5e2ccdc276ac6cc7a4d500c13a319a840da889d81dc757cd277b4

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