Instructions to use NickyNicky/gemma-1.1-2b-it_text_to_sql_format_chatML_V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NickyNicky/gemma-1.1-2b-it_text_to_sql_format_chatML_V1 with Transformers:

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

pipe = pipeline("text-generation", model="NickyNicky/gemma-1.1-2b-it_text_to_sql_format_chatML_V1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NickyNicky/gemma-1.1-2b-it_text_to_sql_format_chatML_V1")
model = AutoModelForCausalLM.from_pretrained("NickyNicky/gemma-1.1-2b-it_text_to_sql_format_chatML_V1", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NickyNicky/gemma-1.1-2b-it_text_to_sql_format_chatML_V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NickyNicky/gemma-1.1-2b-it_text_to_sql_format_chatML_V1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NickyNicky/gemma-1.1-2b-it_text_to_sql_format_chatML_V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NickyNicky/gemma-1.1-2b-it_text_to_sql_format_chatML_V1

SGLang

How to use NickyNicky/gemma-1.1-2b-it_text_to_sql_format_chatML_V1 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 "NickyNicky/gemma-1.1-2b-it_text_to_sql_format_chatML_V1" \
    --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": "NickyNicky/gemma-1.1-2b-it_text_to_sql_format_chatML_V1",
		"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 "NickyNicky/gemma-1.1-2b-it_text_to_sql_format_chatML_V1" \
        --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": "NickyNicky/gemma-1.1-2b-it_text_to_sql_format_chatML_V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NickyNicky/gemma-1.1-2b-it_text_to_sql_format_chatML_V1 with Docker Model Runner:
```
docker model run hf.co/NickyNicky/gemma-1.1-2b-it_text_to_sql_format_chatML_V1
```

NickyNicky commited on Apr 7, 2024

Commit

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Update README.md

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@@ -26,12 +26,6 @@ widget:
   <img src="https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/Fy2GvAheKCnd3Jj3TbrPG.png" alt="" style="width: 95%; max-height: 750px;">
 </p>
-## $ train
-<p align="center">
-  <img src="https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/tLhUyV2uW44-Cf0vwXFbd.png" alt="" style="width: 95%; max-height: 750px;">
-</p>
 ## Metrics.
 ```
 TrainOutput(global_step=2509,
@@ -64,4 +58,9 @@ https://colab.research.google.com/drive/1-0PsRAqTum2UuvsXb9JtXxLISIUWd8zv?usp=sh
 ## gradio colab.
 <p align="center">
   <img src="https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/X2rf3Z7jJ2WUI7LANw4IH.png" alt="" style="width: 95%; max-height: 750px;">
 </p>

   <img src="https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/Fy2GvAheKCnd3Jj3TbrPG.png" alt="" style="width: 95%; max-height: 750px;">
 </p>
 ## Metrics.
 ```
 TrainOutput(global_step=2509,
 ## gradio colab.
 <p align="center">
   <img src="https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/X2rf3Z7jJ2WUI7LANw4IH.png" alt="" style="width: 95%; max-height: 750px;">
+</p>
+## $ train
+<p align="center">
+  <img src="https://cdn-uploads.huggingface.co/production/uploads/641b435ba5f876fe30c5ae0a/tLhUyV2uW44-Cf0vwXFbd.png" alt="" style="width: 95%; max-height: 750px;">
 </p>