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

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
```

gemma-2b_text_to_sql

This model is a fine-tuned version of google/gemma-2b on b-mc2/sql-create-context dataset .

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0002
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 2
training_steps: 25
mixed_precision_training: Native AMP

Training results

Framework versions

PEFT 0.8.2
Transformers 4.38.0
Pytorch 2.2.1+cu121
Datasets 2.17.0
Tokenizers 0.15.2

Downloads last month: 13

Safetensors

Model size

3B params

Tensor type

F32

Model tree for singhjagpreet/gemma-2b_text_to_sql

Base model

google/gemma-2b

Quantized

(35)

this model

singhjagpreet
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gemma-2b_text_to_sql

gemma-2b_text_to_sql

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for singhjagpreet/gemma-2b_text_to_sql

Dataset used to train singhjagpreet/gemma-2b_text_to_sql