Text Generation
Transformers
Safetensors
Spanish
phi3
sql
code-generation
phi-3
fine-tuned
conversational
custom_code
text-generation-inference
Instructions to use Xonotic/phi-3-mini-sql-assistant-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Xonotic/phi-3-mini-sql-assistant-full with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Xonotic/phi-3-mini-sql-assistant-full", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Xonotic/phi-3-mini-sql-assistant-full", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("Xonotic/phi-3-mini-sql-assistant-full", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Xonotic/phi-3-mini-sql-assistant-full with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Xonotic/phi-3-mini-sql-assistant-full" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Xonotic/phi-3-mini-sql-assistant-full", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Xonotic/phi-3-mini-sql-assistant-full
- SGLang
How to use Xonotic/phi-3-mini-sql-assistant-full 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 "Xonotic/phi-3-mini-sql-assistant-full" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Xonotic/phi-3-mini-sql-assistant-full", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Xonotic/phi-3-mini-sql-assistant-full" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Xonotic/phi-3-mini-sql-assistant-full", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Xonotic/phi-3-mini-sql-assistant-full with Docker Model Runner:
docker model run hf.co/Xonotic/phi-3-mini-sql-assistant-full
Subiendo modelo Phi-3 fusionado completo para deploy
Browse filesModelo completo fusionado para generaci贸n de SQL - Listo para deploy
model-00001-of-00002.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 4972489200
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e6d388f8d27ccb5de96019c645e090f98f44c37a31652da740fde04d2a7f703f
|
| 3 |
size 4972489200
|
model-00002-of-00002.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 2669692488
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:fa52ad3d03b830ccc7ee23ab2118d9e057485e19212921f932081204e286068d
|
| 3 |
size 2669692488
|