Instructions to use DiscoResearch/DiscoLM-70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DiscoResearch/DiscoLM-70b with Transformers:

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

pipe = pipeline("text-generation", model="DiscoResearch/DiscoLM-70b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DiscoResearch/DiscoLM-70b")
model = AutoModelForCausalLM.from_pretrained("DiscoResearch/DiscoLM-70b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DiscoResearch/DiscoLM-70b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DiscoResearch/DiscoLM-70b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DiscoResearch/DiscoLM-70b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DiscoResearch/DiscoLM-70b

SGLang

How to use DiscoResearch/DiscoLM-70b 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 "DiscoResearch/DiscoLM-70b" \
    --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": "DiscoResearch/DiscoLM-70b",
		"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 "DiscoResearch/DiscoLM-70b" \
        --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": "DiscoResearch/DiscoLM-70b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DiscoResearch/DiscoLM-70b with Docker Model Runner:
```
docker model run hf.co/DiscoResearch/DiscoLM-70b
```

bjoernp commited on Dec 5, 2023

Commit

f7a3313

1 Parent(s): 5eab2c8

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@@ -34,9 +34,10 @@ text, strengthening it's multilingual capabilities while retaining (and partiall
 This was then further finetuned on a combination of some the most popular open-source instruction sets.
 DiscoLM 70b is a [DiscoResearch](https://huggingface.co/DiscoResearch) project and was trained by [Björn Plüster](https://huggingface.co/bjoernp).
-The model was trained with compute provided by [HessianAI](https://hessian.ai/) - we are very grateful for their support; please check out their wesbite and projects!
 <img src="https://hessian.ai/wp-content/themes/hessianai/img/hessian-ai-logo.svg" width="120">
 ## Table of Contents

 This was then further finetuned on a combination of some the most popular open-source instruction sets.
 DiscoLM 70b is a [DiscoResearch](https://huggingface.co/DiscoResearch) project and was trained by [Björn Plüster](https://huggingface.co/bjoernp).
+The model was trained with compute provided by [HessianAI](https://hessian.ai/) in collaboration with [LAION](https://laion.ai) - we are very grateful for their support; please check out their wesbite and projects!
 <img src="https://hessian.ai/wp-content/themes/hessianai/img/hessian-ai-logo.svg" width="120">
+<img src="https://avatars.githubusercontent.com/u/92627801?s=200&v=4" width="120">
 ## Table of Contents