Instructions to use EricWesthoff/phi-1_5-finetuned-SQL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EricWesthoff/phi-1_5-finetuned-SQL with Transformers:

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

pipe = pipeline("text-generation", model="EricWesthoff/phi-1_5-finetuned-SQL", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("EricWesthoff/phi-1_5-finetuned-SQL", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EricWesthoff/phi-1_5-finetuned-SQL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EricWesthoff/phi-1_5-finetuned-SQL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EricWesthoff/phi-1_5-finetuned-SQL",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EricWesthoff/phi-1_5-finetuned-SQL

SGLang

How to use EricWesthoff/phi-1_5-finetuned-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 "EricWesthoff/phi-1_5-finetuned-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": "EricWesthoff/phi-1_5-finetuned-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 "EricWesthoff/phi-1_5-finetuned-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": "EricWesthoff/phi-1_5-finetuned-SQL",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use EricWesthoff/phi-1_5-finetuned-SQL with Docker Model Runner:
```
docker model run hf.co/EricWesthoff/phi-1_5-finetuned-SQL
```

phi-1_5-finetuned-SQL / README.md

EricWesthoff

End of training

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	---
	license: other
	base_model: microsoft/phi-1_5
	tags:
	- generated_from_trainer
	model-index:
	- name: phi-1_5-finetuned-SQL
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# phi-1_5-finetuned-SQL

	This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3403

	## 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: 4
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: cosine
	- training_steps: 6000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|
	\| 2.4485 \| 0.4 \| 100 \| 2.0478 \|
	\| 2.0521 \| 0.8 \| 200 \| 1.9223 \|
	\| 1.9626 \| 1.2 \| 300 \| 1.8386 \|
	\| 1.8707 \| 1.6 \| 400 \| 1.7702 \|
	\| 1.79 \| 2.0 \| 500 \| 1.7149 \|
	\| 1.7197 \| 2.4 \| 600 \| 1.6567 \|
	\| 1.6904 \| 2.8 \| 700 \| 1.6055 \|
	\| 1.6379 \| 3.2 \| 800 \| 1.5583 \|
	\| 1.5794 \| 3.6 \| 900 \| 1.5267 \|
	\| 1.5977 \| 4.0 \| 1000 \| 1.4928 \|
	\| 1.4773 \| 4.4 \| 1100 \| 1.4638 \|
	\| 1.5185 \| 4.8 \| 1200 \| 1.4446 \|
	\| 1.4476 \| 5.2 \| 1300 \| 1.4337 \|
	\| 1.4321 \| 5.6 \| 1400 \| 1.4287 \|
	\| 1.4393 \| 6.0 \| 1500 \| 1.4282 \|
	\| 1.4956 \| 6.4 \| 1600 \| 1.4504 \|
	\| 1.5252 \| 6.8 \| 1700 \| 1.4311 \|
	\| 1.4864 \| 7.2 \| 1800 \| 1.3654 \|
	\| 1.4092 \| 7.6 \| 1900 \| 1.3112 \|
	\| 1.4063 \| 8.0 \| 2000 \| 1.2925 \|
	\| 1.2657 \| 8.4 \| 2100 \| 1.2123 \|
	\| 1.312 \| 8.8 \| 2200 \| 1.1824 \|
	\| 1.2451 \| 9.2 \| 2300 \| 1.1223 \|
	\| 1.1777 \| 9.6 \| 2400 \| 1.0857 \|
	\| 1.1913 \| 10.0 \| 2500 \| 1.0422 \|
	\| 1.0452 \| 10.4 \| 2600 \| 0.9842 \|
	\| 1.082 \| 10.8 \| 2700 \| 0.9442 \|
	\| 0.9814 \| 11.2 \| 2800 \| 0.9002 \|
	\| 0.9496 \| 11.6 \| 2900 \| 0.8559 \|
	\| 0.9639 \| 12.0 \| 3000 \| 0.8163 \|
	\| 0.823 \| 12.4 \| 3100 \| 0.7827 \|
	\| 0.8395 \| 12.8 \| 3200 \| 0.7384 \|
	\| 0.8038 \| 13.2 \| 3300 \| 0.6971 \|
	\| 0.7458 \| 13.6 \| 3400 \| 0.6641 \|
	\| 0.7495 \| 14.0 \| 3500 \| 0.6328 \|
	\| 0.6575 \| 14.4 \| 3600 \| 0.6017 \|
	\| 0.6448 \| 14.8 \| 3700 \| 0.5829 \|
	\| 0.6268 \| 15.2 \| 3800 \| 0.5412 \|
	\| 0.5738 \| 15.6 \| 3900 \| 0.5233 \|
	\| 0.5989 \| 16.0 \| 4000 \| 0.5008 \|
	\| 0.5033 \| 16.4 \| 4100 \| 0.4781 \|
	\| 0.5343 \| 16.8 \| 4200 \| 0.4572 \|
	\| 0.4881 \| 17.2 \| 4300 \| 0.4390 \|
	\| 0.4676 \| 17.6 \| 4400 \| 0.4254 \|
	\| 0.4683 \| 18.0 \| 4500 \| 0.4171 \|
	\| 0.4188 \| 18.4 \| 4600 \| 0.3987 \|
	\| 0.4245 \| 18.8 \| 4700 \| 0.3869 \|
	\| 0.4136 \| 19.2 \| 4800 \| 0.3777 \|
	\| 0.3938 \| 19.6 \| 4900 \| 0.3694 \|
	\| 0.3986 \| 20.0 \| 5000 \| 0.3627 \|
	\| 0.3661 \| 20.4 \| 5100 \| 0.3571 \|
	\| 0.3743 \| 20.8 \| 5200 \| 0.3516 \|
	\| 0.3668 \| 21.2 \| 5300 \| 0.3482 \|
	\| 0.3613 \| 21.6 \| 5400 \| 0.3455 \|
	\| 0.3542 \| 22.0 \| 5500 \| 0.3430 \|
	\| 0.3505 \| 22.4 \| 5600 \| 0.3419 \|
	\| 0.3495 \| 22.8 \| 5700 \| 0.3410 \|
	\| 0.3396 \| 23.2 \| 5800 \| 0.3405 \|
	\| 0.3481 \| 23.6 \| 5900 \| 0.3403 \|
	\| 0.3444 \| 24.0 \| 6000 \| 0.3403 \|


	### Framework versions

	- Transformers 4.34.1
	- Pytorch 2.1.0+cu118
	- Datasets 2.14.6
	- Tokenizers 0.14.1

	---
	license: other
	base_model: microsoft/phi-1_5
	tags:
	- generated_from_trainer
	model-index:
	- name: phi-1_5-finetuned-SQL
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# phi-1_5-finetuned-SQL

	This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3403

	## 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: 4
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: cosine
	- training_steps: 6000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|
	\| 2.4485 \| 0.4 \| 100 \| 2.0478 \|
	\| 2.0521 \| 0.8 \| 200 \| 1.9223 \|
	\| 1.9626 \| 1.2 \| 300 \| 1.8386 \|
	\| 1.8707 \| 1.6 \| 400 \| 1.7702 \|
	\| 1.79 \| 2.0 \| 500 \| 1.7149 \|
	\| 1.7197 \| 2.4 \| 600 \| 1.6567 \|
	\| 1.6904 \| 2.8 \| 700 \| 1.6055 \|
	\| 1.6379 \| 3.2 \| 800 \| 1.5583 \|
	\| 1.5794 \| 3.6 \| 900 \| 1.5267 \|
	\| 1.5977 \| 4.0 \| 1000 \| 1.4928 \|
	\| 1.4773 \| 4.4 \| 1100 \| 1.4638 \|
	\| 1.5185 \| 4.8 \| 1200 \| 1.4446 \|
	\| 1.4476 \| 5.2 \| 1300 \| 1.4337 \|
	\| 1.4321 \| 5.6 \| 1400 \| 1.4287 \|
	\| 1.4393 \| 6.0 \| 1500 \| 1.4282 \|
	\| 1.4956 \| 6.4 \| 1600 \| 1.4504 \|
	\| 1.5252 \| 6.8 \| 1700 \| 1.4311 \|
	\| 1.4864 \| 7.2 \| 1800 \| 1.3654 \|
	\| 1.4092 \| 7.6 \| 1900 \| 1.3112 \|
	\| 1.4063 \| 8.0 \| 2000 \| 1.2925 \|
	\| 1.2657 \| 8.4 \| 2100 \| 1.2123 \|
	\| 1.312 \| 8.8 \| 2200 \| 1.1824 \|
	\| 1.2451 \| 9.2 \| 2300 \| 1.1223 \|
	\| 1.1777 \| 9.6 \| 2400 \| 1.0857 \|
	\| 1.1913 \| 10.0 \| 2500 \| 1.0422 \|
	\| 1.0452 \| 10.4 \| 2600 \| 0.9842 \|
	\| 1.082 \| 10.8 \| 2700 \| 0.9442 \|
	\| 0.9814 \| 11.2 \| 2800 \| 0.9002 \|
	\| 0.9496 \| 11.6 \| 2900 \| 0.8559 \|
	\| 0.9639 \| 12.0 \| 3000 \| 0.8163 \|
	\| 0.823 \| 12.4 \| 3100 \| 0.7827 \|
	\| 0.8395 \| 12.8 \| 3200 \| 0.7384 \|
	\| 0.8038 \| 13.2 \| 3300 \| 0.6971 \|
	\| 0.7458 \| 13.6 \| 3400 \| 0.6641 \|
	\| 0.7495 \| 14.0 \| 3500 \| 0.6328 \|
	\| 0.6575 \| 14.4 \| 3600 \| 0.6017 \|
	\| 0.6448 \| 14.8 \| 3700 \| 0.5829 \|
	\| 0.6268 \| 15.2 \| 3800 \| 0.5412 \|
	\| 0.5738 \| 15.6 \| 3900 \| 0.5233 \|
	\| 0.5989 \| 16.0 \| 4000 \| 0.5008 \|
	\| 0.5033 \| 16.4 \| 4100 \| 0.4781 \|
	\| 0.5343 \| 16.8 \| 4200 \| 0.4572 \|
	\| 0.4881 \| 17.2 \| 4300 \| 0.4390 \|
	\| 0.4676 \| 17.6 \| 4400 \| 0.4254 \|
	\| 0.4683 \| 18.0 \| 4500 \| 0.4171 \|
	\| 0.4188 \| 18.4 \| 4600 \| 0.3987 \|
	\| 0.4245 \| 18.8 \| 4700 \| 0.3869 \|
	\| 0.4136 \| 19.2 \| 4800 \| 0.3777 \|
	\| 0.3938 \| 19.6 \| 4900 \| 0.3694 \|
	\| 0.3986 \| 20.0 \| 5000 \| 0.3627 \|
	\| 0.3661 \| 20.4 \| 5100 \| 0.3571 \|
	\| 0.3743 \| 20.8 \| 5200 \| 0.3516 \|
	\| 0.3668 \| 21.2 \| 5300 \| 0.3482 \|
	\| 0.3613 \| 21.6 \| 5400 \| 0.3455 \|
	\| 0.3542 \| 22.0 \| 5500 \| 0.3430 \|
	\| 0.3505 \| 22.4 \| 5600 \| 0.3419 \|
	\| 0.3495 \| 22.8 \| 5700 \| 0.3410 \|
	\| 0.3396 \| 23.2 \| 5800 \| 0.3405 \|
	\| 0.3481 \| 23.6 \| 5900 \| 0.3403 \|
	\| 0.3444 \| 24.0 \| 6000 \| 0.3403 \|


	### Framework versions

	- Transformers 4.34.1
	- Pytorch 2.1.0+cu118
	- Datasets 2.14.6
	- Tokenizers 0.14.1