Instructions to use qubing/text2sql_bcft_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qubing/text2sql_bcft_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="qubing/text2sql_bcft_v1", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("qubing/text2sql_bcft_v1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use qubing/text2sql_bcft_v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "qubing/text2sql_bcft_v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "qubing/text2sql_bcft_v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/qubing/text2sql_bcft_v1
- SGLang
How to use qubing/text2sql_bcft_v1 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 "qubing/text2sql_bcft_v1" \ --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": "qubing/text2sql_bcft_v1", "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 "qubing/text2sql_bcft_v1" \ --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": "qubing/text2sql_bcft_v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use qubing/text2sql_bcft_v1 with Docker Model Runner:
docker model run hf.co/qubing/text2sql_bcft_v1
| # Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved. | |
| from transformers.configuration_utils import PretrainedConfig | |
| class BaichuanConfig(PretrainedConfig): | |
| model_type = "baichuan" | |
| keys_to_ignore_at_inference = ["past_key_values"] | |
| def __init__( | |
| self, | |
| vocab_size=64000, | |
| hidden_size=5120, | |
| intermediate_size=13696, | |
| num_hidden_layers=40, | |
| num_attention_heads=40, | |
| hidden_act="silu", | |
| model_max_length=4096, | |
| initializer_range=0.02, | |
| rms_norm_eps=1e-6, | |
| use_cache=True, | |
| pad_token_id=0, | |
| bos_token_id=1, | |
| eos_token_id=2, | |
| tie_word_embeddings=False, | |
| gradient_checkpointing=False, | |
| **kwargs, | |
| ): | |
| self.vocab_size = vocab_size | |
| self.model_max_length = model_max_length | |
| self.hidden_size = hidden_size | |
| self.intermediate_size = intermediate_size | |
| self.num_hidden_layers = num_hidden_layers | |
| self.num_attention_heads = num_attention_heads | |
| self.hidden_act = hidden_act | |
| self.initializer_range = initializer_range | |
| self.rms_norm_eps = rms_norm_eps | |
| self.use_cache = use_cache | |
| self.gradient_checkpointing = gradient_checkpointing, | |
| super().__init__( | |
| pad_token_id=pad_token_id, | |
| bos_token_id=bos_token_id, | |
| eos_token_id=eos_token_id, | |
| tie_word_embeddings=tie_word_embeddings, | |
| **kwargs, | |
| ) | |