Text Generation
Transformers
TensorBoard
Safetensors
mixformer-sequential
Generated from Trainer
custom_code
Instructions to use machinelearningzuu/phi-1_5-finetuned-sql-injection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use machinelearningzuu/phi-1_5-finetuned-sql-injection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="machinelearningzuu/phi-1_5-finetuned-sql-injection", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("machinelearningzuu/phi-1_5-finetuned-sql-injection", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use machinelearningzuu/phi-1_5-finetuned-sql-injection with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "machinelearningzuu/phi-1_5-finetuned-sql-injection" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "machinelearningzuu/phi-1_5-finetuned-sql-injection", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/machinelearningzuu/phi-1_5-finetuned-sql-injection
- SGLang
How to use machinelearningzuu/phi-1_5-finetuned-sql-injection 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 "machinelearningzuu/phi-1_5-finetuned-sql-injection" \ --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": "machinelearningzuu/phi-1_5-finetuned-sql-injection", "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 "machinelearningzuu/phi-1_5-finetuned-sql-injection" \ --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": "machinelearningzuu/phi-1_5-finetuned-sql-injection", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use machinelearningzuu/phi-1_5-finetuned-sql-injection with Docker Model Runner:
docker model run hf.co/machinelearningzuu/phi-1_5-finetuned-sql-injection
Training in progress, epoch 0
Browse files
adapter_model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 18886256
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:79efd2ceac1ff748d180ff8b61ac809485a429ac1e7be0e77cd042f3ae2ba487
|
| 3 |
size 18886256
|
runs/Nov09_14-28-32_bce867db05a3/events.out.tfevents.1699540112.bce867db05a3.32376.0
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:635a5eb6744ea9eff9b647188efa6b85eaae78980b24ae27a234cac7df239c81
|
| 3 |
+
size 6759
|
training_args.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 4600
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:ffff043aa00ebe647ce6eca79b2ad0cd182ce50e6bd443e6897a19803cc0894a
|
| 3 |
size 4600
|