Instructions to use singhjagpreet/gemma-2b_text_to_sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use singhjagpreet/gemma-2b_text_to_sql with Transformers:

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

pipe = pipeline("text-generation", model="singhjagpreet/gemma-2b_text_to_sql")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("singhjagpreet/gemma-2b_text_to_sql")
model = AutoModelForCausalLM.from_pretrained("singhjagpreet/gemma-2b_text_to_sql")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use singhjagpreet/gemma-2b_text_to_sql with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "singhjagpreet/gemma-2b_text_to_sql"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "singhjagpreet/gemma-2b_text_to_sql",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/singhjagpreet/gemma-2b_text_to_sql

SGLang

How to use singhjagpreet/gemma-2b_text_to_sql 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 "singhjagpreet/gemma-2b_text_to_sql" \
    --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": "singhjagpreet/gemma-2b_text_to_sql",
		"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 "singhjagpreet/gemma-2b_text_to_sql" \
        --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": "singhjagpreet/gemma-2b_text_to_sql",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use singhjagpreet/gemma-2b_text_to_sql with Docker Model Runner:
```
docker model run hf.co/singhjagpreet/gemma-2b_text_to_sql
```

singhjagpreet commited on Apr 9, 2024

Commit

12ff78e

verified ·

1 Parent(s): a21912c

Update README.md

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README.md +7 -2

README.md CHANGED Viewed

@@ -2,8 +2,8 @@
 library_name: transformers
 base_model: google/gemma-2b
 model-index:
-  - name: singhjagpreet/gemma-2b_text_to_sql
-    results: []
 inference:
   parameters:
     do_sample: false
@@ -29,6 +29,11 @@ metrics:
 - rouge
 datasets:
 - b-mc2/sql-create-context
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You

 library_name: transformers
 base_model: google/gemma-2b
 model-index:
+- name: singhjagpreet/gemma-2b_text_to_sql
+  results: []
 inference:
   parameters:
     do_sample: false
 - rouge
 datasets:
 - b-mc2/sql-create-context
+license: gemma
+language:
+- en
+tags:
+- code
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You