Instructions to use OEvortex/HelpingAI-Lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OEvortex/HelpingAI-Lite with Transformers:

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

pipe = pipeline("text-generation", model="OEvortex/HelpingAI-Lite")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OEvortex/HelpingAI-Lite")
model = AutoModelForCausalLM.from_pretrained("OEvortex/HelpingAI-Lite")
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 OEvortex/HelpingAI-Lite with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/OEvortex/HelpingAI-Lite

SGLang

How to use OEvortex/HelpingAI-Lite 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 "OEvortex/HelpingAI-Lite" \
    --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": "OEvortex/HelpingAI-Lite",
		"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 "OEvortex/HelpingAI-Lite" \
        --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": "OEvortex/HelpingAI-Lite",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use OEvortex/HelpingAI-Lite with Docker Model Runner:
```
docker model run hf.co/OEvortex/HelpingAI-Lite
```

Abhaykoul commited on Jan 14, 2024

Commit

0b88c68

verified ·

1 Parent(s): dcd8f4d

Update README.md

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README.md +14 -2

README.md CHANGED Viewed

@@ -45,13 +45,19 @@ The model supports English language.
 ## Usage
-# CPU
 ```python
 from transformers import pipeline
-pipe = pipeline("text-generation", model="OEvortex/HelpingAI-Lite")
 messages = [
     {
         "role": "system",
@@ -63,7 +69,13 @@ messages = [
     },
 ]
 prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
 outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
 print(outputs[0]["generated_text"])
 ```

 ## Usage
+# CPU and GPU code
 ```python
 from transformers import pipeline
+from accelerate import Accelerator
+# Initialize the accelerator
+accelerator = Accelerator()
+# Initialize the pipeline
+pipe = pipeline("text-generation", model="OEvortex/HelpingAI-Lite", device=accelerator.device)
+# Define the messages
 messages = [
     {
         "role": "system",
     },
 ]
+# Prepare the prompt
 prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+# Generate predictions
 outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+# Print the generated text
 print(outputs[0]["generated_text"])
 ```