Instructions to use lamahugface/sqlcoder-stackexchange with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lamahugface/sqlcoder-stackexchange with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("defog/sqlcoder-7b") model = PeftModel.from_pretrained(base_model, "lamahugface/sqlcoder-stackexchange") - Transformers
How to use lamahugface/sqlcoder-stackexchange with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lamahugface/sqlcoder-stackexchange")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("lamahugface/sqlcoder-stackexchange", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use lamahugface/sqlcoder-stackexchange with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lamahugface/sqlcoder-stackexchange" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lamahugface/sqlcoder-stackexchange", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lamahugface/sqlcoder-stackexchange
- SGLang
How to use lamahugface/sqlcoder-stackexchange 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 "lamahugface/sqlcoder-stackexchange" \ --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": "lamahugface/sqlcoder-stackexchange", "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 "lamahugface/sqlcoder-stackexchange" \ --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": "lamahugface/sqlcoder-stackexchange", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lamahugface/sqlcoder-stackexchange with Docker Model Runner:
docker model run hf.co/lamahugface/sqlcoder-stackexchange
File size: 1,446 Bytes
19eca80 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | {
"best_global_step": 29,
"best_metric": 0.09725816547870636,
"best_model_checkpoint": "finetune/sqlcoder-finetuned\\checkpoint-29",
"epoch": 1.0,
"eval_steps": 500,
"global_step": 29,
"is_hyper_param_search": false,
"is_local_process_zero": true,
"is_world_process_zero": true,
"log_history": [
{
"epoch": 0.35398230088495575,
"grad_norm": 1.021061897277832,
"learning_rate": 0.00019882804237803488,
"loss": 0.21164827346801757,
"step": 10
},
{
"epoch": 0.7079646017699115,
"grad_norm": 0.6921543478965759,
"learning_rate": 0.00018595696069872013,
"loss": 0.06274759769439697,
"step": 20
},
{
"epoch": 1.0,
"eval_loss": 0.09725816547870636,
"eval_runtime": 6.4034,
"eval_samples_per_second": 3.904,
"eval_steps_per_second": 2.03,
"step": 29
}
],
"logging_steps": 10,
"max_steps": 87,
"num_input_tokens_seen": 0,
"num_train_epochs": 3,
"save_steps": 500,
"stateful_callbacks": {
"TrainerControl": {
"args": {
"should_epoch_stop": false,
"should_evaluate": false,
"should_log": false,
"should_save": true,
"should_training_stop": false
},
"attributes": {}
}
},
"total_flos": 3028108894863360.0,
"train_batch_size": 2,
"trial_name": null,
"trial_params": null
}
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