Instructions to use Xonotic/SQL-Assistant-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Xonotic/SQL-Assistant-finetuned with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("gpt2")
model = PeftModel.from_pretrained(base_model, "Xonotic/SQL-Assistant-finetuned")

Transformers

How to use Xonotic/SQL-Assistant-finetuned with Transformers:

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

pipe = pipeline("text-generation", model="Xonotic/SQL-Assistant-finetuned")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Xonotic/SQL-Assistant-finetuned", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Xonotic/SQL-Assistant-finetuned with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Xonotic/SQL-Assistant-finetuned

SGLang

How to use Xonotic/SQL-Assistant-finetuned 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/SQL-Assistant-finetuned" \
    --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": "Xonotic/SQL-Assistant-finetuned",
		"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 "Xonotic/SQL-Assistant-finetuned" \
        --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": "Xonotic/SQL-Assistant-finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Xonotic/SQL-Assistant-finetuned with Docker Model Runner:
```
docker model run hf.co/Xonotic/SQL-Assistant-finetuned
```

SQL-Assistant-finetuned / adapter_config.json

Xonotic

Subiendo modelo fine-tuned para SQL de centros comerciales

a7a8694 verified 7 months ago

raw

history blame contribute delete

837 Bytes

	{
	"alpha_pattern": {},
	"auto_mapping": null,
	"base_model_name_or_path": "gpt2",
	"bias": "none",
	"corda_config": null,
	"eva_config": null,
	"exclude_modules": null,
	"fan_in_fan_out": true,
	"inference_mode": true,
	"init_lora_weights": true,
	"layer_replication": null,
	"layers_pattern": null,
	"layers_to_transform": null,
	"loftq_config": {},
	"lora_alpha": 16,
	"lora_bias": false,
	"lora_dropout": 0.05,
	"megatron_config": null,
	"megatron_core": "megatron.core",
	"modules_to_save": null,
	"peft_type": "LORA",
	"qalora_group_size": 16,
	"r": 8,
	"rank_pattern": {},
	"revision": null,
	"target_modules": [
	"c_proj",
	"c_attn"
	],
	"target_parameters": null,
	"task_type": "CAUSAL_LM",
	"trainable_token_indices": null,
	"use_dora": false,
	"use_qalora": false,
	"use_rslora": false
	}