Instructions to use defog/sqlcoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use defog/sqlcoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="defog/sqlcoder")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("defog/sqlcoder") model = AutoModelForCausalLM.from_pretrained("defog/sqlcoder") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use defog/sqlcoder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "defog/sqlcoder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "defog/sqlcoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/defog/sqlcoder
- SGLang
How to use defog/sqlcoder 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 "defog/sqlcoder" \ --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": "defog/sqlcoder", "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 "defog/sqlcoder" \ --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": "defog/sqlcoder", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use defog/sqlcoder with Docker Model Runner:
docker model run hf.co/defog/sqlcoder
| { | |
| "_name_or_path": "defog/starcoder-finetune-v3-easy", | |
| "activation_function": "gelu", | |
| "architectures": [ | |
| "GPTBigCodeForCausalLM" | |
| ], | |
| "attention_softmax_in_fp32": true, | |
| "attn_pdrop": 0.1, | |
| "bos_token_id": 0, | |
| "embd_pdrop": 0.1, | |
| "eos_token_id": 0, | |
| "inference_runner": 0, | |
| "initializer_range": 0.02, | |
| "layer_norm_epsilon": 1e-05, | |
| "max_batch_size": null, | |
| "max_sequence_length": null, | |
| "model_type": "gpt_bigcode", | |
| "multi_query": true, | |
| "n_embd": 6144, | |
| "n_head": 48, | |
| "n_inner": 24576, | |
| "n_layer": 40, | |
| "n_positions": 8192, | |
| "pad_key_length": true, | |
| "pre_allocate_kv_cache": false, | |
| "resid_pdrop": 0.1, | |
| "scale_attention_softmax_in_fp32": true, | |
| "scale_attn_weights": true, | |
| "summary_activation": null, | |
| "summary_first_dropout": 0.1, | |
| "summary_proj_to_labels": true, | |
| "summary_type": "cls_index", | |
| "summary_use_proj": true, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.31.0", | |
| "use_cache": true, | |
| "validate_runner_input": true, | |
| "vocab_size": 49152 | |
| } | |