Instructions to use sieg2011/codet5-base-sql-create-context with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sieg2011/codet5-base-sql-create-context with Transformers:

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

pipe = pipeline("text-generation", model="sieg2011/codet5-base-sql-create-context")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("sieg2011/codet5-base-sql-create-context")
model = AutoModelForSeq2SeqLM.from_pretrained("sieg2011/codet5-base-sql-create-context")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sieg2011/codet5-base-sql-create-context with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sieg2011/codet5-base-sql-create-context"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sieg2011/codet5-base-sql-create-context",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sieg2011/codet5-base-sql-create-context

SGLang

How to use sieg2011/codet5-base-sql-create-context 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 "sieg2011/codet5-base-sql-create-context" \
    --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": "sieg2011/codet5-base-sql-create-context",
		"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 "sieg2011/codet5-base-sql-create-context" \
        --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": "sieg2011/codet5-base-sql-create-context",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sieg2011/codet5-base-sql-create-context with Docker Model Runner:
```
docker model run hf.co/sieg2011/codet5-base-sql-create-context
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

codet5-base-sql-create-context

This model is a fine-tuned version of Salesforce/codet5-base on b-mc2/sql-create-context dataset. It achieves the following results on the evaluation set:

Loss: 0.0036
Exact Match: 0.2451
Rouge2: 0.8839
Bleu: 75.6780

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Exact Match	Rouge2	Bleu
0.0071	1.0	7858	0.0047	0.2360	0.8808	75.1891
0.0041	2.0	15716	0.0039	0.2393	0.8820	75.4061
0.0028	3.0	23574	0.0037	0.2447	0.8832	75.5863
0.0019	4.0	31432	0.0035	0.2469	0.8837	75.7130
0.0013	5.0	39290	0.0036	0.2451	0.8839	75.6780

Framework versions

Transformers 4.50.3
Pytorch 2.5.1+cu121
Datasets 3.5.0
Tokenizers 0.21.1

Downloads last month: 1

Safetensors

Model size

0.2B params

Tensor type

F32

Model tree for sieg2011/codet5-base-sql-create-context

Base model

Salesforce/codet5-base

Finetuned

(93)

this model

sieg2011
/

codet5-base-sql-create-context

codet5-base-sql-create-context

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for sieg2011/codet5-base-sql-create-context

Dataset used to train sieg2011/codet5-base-sql-create-context