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

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
```

Error deploying to Sagemaker

by neke-leo - opened Aug 21, 2023

Discussion

neke-leo

Aug 21, 2023

I tried to deploy the model to SageMaker with the example code provided, but I get an error. I think it might be some version mismatch, but I'm not sure how to fix it, given that the packages installed depend on the hugging face image and not on anything I use in my Sagemaker notebook(sagemaker-2.178.0).

File "/opt/conda/lib/python3.9/site-packages/text_generation_server/server.py", line 142, in serve_inner
model = get_model(
File "/opt/conda/lib/python3.9/site-packages/text_generation_server/models/init.py", line 145, in get_model
return BLOOMSharded(
File "/opt/conda/lib/python3.9/site-packages/text_generation_server/models/bloom.py", line 80, in init
model = BloomForCausalLM(config, weights)
File "/opt/conda/lib/python3.9/site-packages/text_generation_server/models/custom_modeling/bloom_modeling.py", line 818, in init
self.transformer = BloomModel(config, weights)
File "/opt/conda/lib/python3.9/site-packages/text_generation_server/models/custom_modeling/bloom_modeling.py", line 609, in init
self.word_embeddings = TensorParallelEmbedding(
File "/opt/conda/lib/python3.9/site-packages/text_generation_server/utils/layers.py", line 293, in init
weight = weights.get_partial_sharded(f"{prefix}.weight", dim=0)
File "/opt/conda/lib/python3.9/site-packages/text_generation_server/utils/weights.py", line 73, in get_partial_sharded
filename, tensor_name = self.get_filename(tensor_name)
File "/opt/conda/lib/python3.9/site-packages/text_generation_server/utils/weights.py", line 49, in get_filename
raise RuntimeError(f"weight {tensor_name} does not exist")
RuntimeError: weight word_embeddings.weight does not exist

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