Instructions to use Edentns/DataVortexS-10.7B-dpo-v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Edentns/DataVortexS-10.7B-dpo-v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="Edentns/DataVortexS-10.7B-dpo-v1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.0")
model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.0", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Edentns/DataVortexS-10.7B-dpo-v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Edentns/DataVortexS-10.7B-dpo-v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Edentns/DataVortexS-10.7B-dpo-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Edentns/DataVortexS-10.7B-dpo-v1.0

SGLang

How to use Edentns/DataVortexS-10.7B-dpo-v1.0 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 "Edentns/DataVortexS-10.7B-dpo-v1.0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Edentns/DataVortexS-10.7B-dpo-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Edentns/DataVortexS-10.7B-dpo-v1.0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Edentns/DataVortexS-10.7B-dpo-v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Edentns/DataVortexS-10.7B-dpo-v1.0 with Docker Model Runner:
```
docker model run hf.co/Edentns/DataVortexS-10.7B-dpo-v1.0
```

Response code 424

by h2ku - opened Feb 15, 2024

Discussion

h2ku

Feb 15, 2024

I got following error msg running on Nvidia Tesla T4 (4x GPU · 64 GB). Which basically saying OOM.

{"error":"Request failed during generation: Server error: CUDA out of memory. Tried to allocate 72.00 MiB. GPU 0 has a total capacty of 14.58 GiB of which 59.56 MiB is free. Process 17070 has 14.52 GiB memory in use. Of the allocated memory 13.84 GiB is allocated by PyTorch, and 462.28 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF","error_type":"generation"}

banb3515

Feb 15, 2024

@h2ku The example code written in Readme loads the model to only one GPU by default.
Change the part that loads the model as follows.

model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.0", device_map="auto")

h2ku

Feb 15, 2024

I was leaving the comment for fyi to future users.

Unfortunately, I'm working on a HF Inference Endpoints. Which does not provide function to include such options.
Thank you for the tip :)

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