Instructions to use philschmid/instruct-igel-001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use philschmid/instruct-igel-001 with Transformers:

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

pipe = pipeline("text-generation", model="philschmid/instruct-igel-001")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("philschmid/instruct-igel-001")
model = AutoModelForCausalLM.from_pretrained("philschmid/instruct-igel-001")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use philschmid/instruct-igel-001 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "philschmid/instruct-igel-001"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "philschmid/instruct-igel-001",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/philschmid/instruct-igel-001

SGLang

How to use philschmid/instruct-igel-001 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 "philschmid/instruct-igel-001" \
    --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": "philschmid/instruct-igel-001",
		"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 "philschmid/instruct-igel-001" \
        --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": "philschmid/instruct-igel-001",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use philschmid/instruct-igel-001 with Docker Model Runner:
```
docker model run hf.co/philschmid/instruct-igel-001
```

philschmid commited on Oct 27, 2023

Commit

7919de9

1 Parent(s): 0d4f138

Update README.md

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@@ -8,7 +8,8 @@ tags:
 - LLM
 inference: false
 widget:
-- text: "TODO"
 ---
 # IGEL: Instruction-tuned German large Language Model for Text
@@ -49,4 +50,4 @@ _coming soon_
 You can test the model in this LLM playground.
-_coming soon_

 - LLM
 inference: false
 widget:
+- text: TODO
+license: bigscience-openrail-m
 ---
 # IGEL: Instruction-tuned German large Language Model for Text
 You can test the model in this LLM playground.
+_coming soon_