Instructions to use Lagstill/Varsity_module2_bot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lagstill/Varsity_module2_bot with Transformers:

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

pipe = pipeline("text-generation", model="Lagstill/Varsity_module2_bot")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lagstill/Varsity_module2_bot")
model = AutoModelForCausalLM.from_pretrained("Lagstill/Varsity_module2_bot")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Lagstill/Varsity_module2_bot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lagstill/Varsity_module2_bot"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lagstill/Varsity_module2_bot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Lagstill/Varsity_module2_bot

SGLang

How to use Lagstill/Varsity_module2_bot 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 "Lagstill/Varsity_module2_bot" \
    --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": "Lagstill/Varsity_module2_bot",
		"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 "Lagstill/Varsity_module2_bot" \
        --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": "Lagstill/Varsity_module2_bot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Lagstill/Varsity_module2_bot with Docker Model Runner:
```
docker model run hf.co/Lagstill/Varsity_module2_bot
```

Lagstill commited on Apr 24, 2024

Commit

d4d3969

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1 Parent(s): c34ad1a

Update README.md

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@@ -1,21 +1,4 @@
 ---
-# license: gemma
-# widget :
-# - text: "What is Evening Star?"
-#   example_title: "What is Evening Star?"
-# - text : "கடுகு சிறுத்தாலும்"
-#   example_title: "கடுகு சிறுத்தாலும்"
-# - text : "யானைக்கும் அடி"
-#   example_title : "யானைக்கும் அடி"
-# ---
-# ---
-library_name: transformers
-extra_gated_heading: Access Gemma on Hugging Face
-extra_gated_prompt: >-
-  To access Gemma on Hugging Face, you’re required to review and agree to
-  Google’s usage license. To do this, please ensure you’re logged-in to Hugging
-  Face and click below. Requests are processed immediately.
-extra_gated_button_content: Acknowledge license
 license: gemma
 inference:
   parameters:
@@ -24,6 +7,8 @@ widget:
 - messages:
   - role: user
     content: What is your favorite condiment?
 ---
 # Gemma Model Card

 ---
 license: gemma
 inference:
   parameters:
 - messages:
   - role: user
     content: What is your favorite condiment?
+language:
+- en
 ---
 # Gemma Model Card