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
```

EricWesthoff commited on Oct 25, 2023

Commit

b3b3eb2

1 Parent(s): 7bfbb2b

End of training

Browse files

Files changed (1) hide show

README.md +19 -9

README.md CHANGED Viewed

@@ -15,7 +15,7 @@ should probably proofread and complete it, then remove this comment. -->
 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: 1.1600
 ## Model description
@@ -40,17 +40,27 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: cosine
-- num_epochs: 5
 ### Training results
-| Training Loss | Epoch | Step  | Validation Loss |
-|:-------------:|:-----:|:-----:|:---------------:|
-| 1.5428        | 1.0   | 12364 | 1.4610          |
-| 1.4145        | 2.0   | 24728 | 1.3385          |
-| 1.3136        | 3.0   | 37092 | 1.2388          |
-| 1.2444        | 4.0   | 49456 | 1.1757          |
-| 1.1791        | 5.0   | 61820 | 1.1600          |
 ### Framework versions

 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: 1.4282
 ## Model description
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: cosine
+- training_steps: 1500
 ### 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          |
 ### Framework versions