Instructions to use Akaisora/sql-gen-join with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Akaisora/sql-gen-join with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Akaisora/sql-gen-join")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Akaisora/sql-gen-join") model = AutoModelForCausalLM.from_pretrained("Akaisora/sql-gen-join") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Akaisora/sql-gen-join with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Akaisora/sql-gen-join" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Akaisora/sql-gen-join", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Akaisora/sql-gen-join
- SGLang
How to use Akaisora/sql-gen-join 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 "Akaisora/sql-gen-join" \ --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": "Akaisora/sql-gen-join", "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 "Akaisora/sql-gen-join" \ --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": "Akaisora/sql-gen-join", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Akaisora/sql-gen-join with Docker Model Runner:
docker model run hf.co/Akaisora/sql-gen-join
update model card README.md
Browse files
README.md
CHANGED
|
@@ -14,7 +14,7 @@ should probably proofread and complete it, then remove this comment. -->
|
|
| 14 |
|
| 15 |
This model is a fine-tuned version of [Salesforce/codegen-350M-multi](https://huggingface.co/Salesforce/codegen-350M-multi) on an unknown dataset.
|
| 16 |
It achieves the following results on the evaluation set:
|
| 17 |
-
- Loss: 0.
|
| 18 |
|
| 19 |
## Model description
|
| 20 |
|
|
@@ -47,9 +47,9 @@ The following hyperparameters were used during training:
|
|
| 47 |
|
| 48 |
| Training Loss | Epoch | Step | Validation Loss |
|
| 49 |
|:-------------:|:-----:|:----:|:---------------:|
|
| 50 |
-
| No log | 0.99 |
|
| 51 |
-
| No log | 1.99 |
|
| 52 |
-
| No log | 2.
|
| 53 |
|
| 54 |
|
| 55 |
### Framework versions
|
|
|
|
| 14 |
|
| 15 |
This model is a fine-tuned version of [Salesforce/codegen-350M-multi](https://huggingface.co/Salesforce/codegen-350M-multi) on an unknown dataset.
|
| 16 |
It achieves the following results on the evaluation set:
|
| 17 |
+
- Loss: 0.0339
|
| 18 |
|
| 19 |
## Model description
|
| 20 |
|
|
|
|
| 47 |
|
| 48 |
| Training Loss | Epoch | Step | Validation Loss |
|
| 49 |
|:-------------:|:-----:|:----:|:---------------:|
|
| 50 |
+
| No log | 0.99 | 106 | 0.0525 |
|
| 51 |
+
| No log | 1.99 | 213 | 0.0397 |
|
| 52 |
+
| No log | 2.97 | 318 | 0.0339 |
|
| 53 |
|
| 54 |
|
| 55 |
### Framework versions
|