Instructions to use tamilanda/my-sql-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tamilanda/my-sql-model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("codellama/CodeLlama-7b-Instruct-hf") model = PeftModel.from_pretrained(base_model, "tamilanda/my-sql-model") - Transformers
How to use tamilanda/my-sql-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tamilanda/my-sql-model") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tamilanda/my-sql-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tamilanda/my-sql-model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tamilanda/my-sql-model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tamilanda/my-sql-model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tamilanda/my-sql-model
- SGLang
How to use tamilanda/my-sql-model 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 "tamilanda/my-sql-model" \ --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": "tamilanda/my-sql-model", "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 "tamilanda/my-sql-model" \ --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": "tamilanda/my-sql-model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use tamilanda/my-sql-model with Docker Model Runner:
docker model run hf.co/tamilanda/my-sql-model
Download checkpoint-200/trainer_state.json from tamilanda/my-sql-model: direct link, hf CLI and curl.
- Browser
- Download file 2.47 kB
-
https://huggingface.co/tamilanda/my-sql-model/resolve/main/checkpoint-200/trainer_state.json
- Command line
-
hf download hf://tamilanda/my-sql-model/checkpoint-200/trainer_state.json
-
curl -L -o trainer_state.json https://huggingface.co/tamilanda/my-sql-model/resolve/main/checkpoint-200/trainer_state.json
2.47 kB
| { | |
| "best_global_step": null, | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 0.8, | |
| "eval_steps": 200, | |
| "global_step": 200, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.08, | |
| "grad_norm": 10.467041015625, | |
| "learning_rate": 4.6200000000000005e-05, | |
| "loss": 2.8162, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 0.16, | |
| "grad_norm": 3.9829273223876953, | |
| "learning_rate": 4.22e-05, | |
| "loss": 1.9308, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 0.24, | |
| "grad_norm": 4.000159740447998, | |
| "learning_rate": 3.82e-05, | |
| "loss": 1.6849, | |
| "step": 60 | |
| }, | |
| { | |
| "epoch": 0.32, | |
| "grad_norm": 1.9223499298095703, | |
| "learning_rate": 3.4200000000000005e-05, | |
| "loss": 1.5359, | |
| "step": 80 | |
| }, | |
| { | |
| "epoch": 0.4, | |
| "grad_norm": 1.8478671312332153, | |
| "learning_rate": 3.02e-05, | |
| "loss": 1.571, | |
| "step": 100 | |
| }, | |
| { | |
| "epoch": 0.48, | |
| "grad_norm": 2.243217945098877, | |
| "learning_rate": 2.6200000000000003e-05, | |
| "loss": 1.5199, | |
| "step": 120 | |
| }, | |
| { | |
| "epoch": 0.56, | |
| "grad_norm": 2.793548345565796, | |
| "learning_rate": 2.22e-05, | |
| "loss": 1.5036, | |
| "step": 140 | |
| }, | |
| { | |
| "epoch": 0.64, | |
| "grad_norm": 1.5502959489822388, | |
| "learning_rate": 1.8200000000000002e-05, | |
| "loss": 1.4294, | |
| "step": 160 | |
| }, | |
| { | |
| "epoch": 0.72, | |
| "grad_norm": 5.679831504821777, | |
| "learning_rate": 1.42e-05, | |
| "loss": 1.4503, | |
| "step": 180 | |
| }, | |
| { | |
| "epoch": 0.8, | |
| "grad_norm": 1.3757414817810059, | |
| "learning_rate": 1.02e-05, | |
| "loss": 1.4753, | |
| "step": 200 | |
| }, | |
| { | |
| "epoch": 0.8, | |
| "eval_loss": 1.129705786705017, | |
| "eval_runtime": 1533.624, | |
| "eval_samples_per_second": 0.652, | |
| "eval_steps_per_second": 0.326, | |
| "step": 200 | |
| } | |
| ], | |
| "logging_steps": 20, | |
| "max_steps": 250, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 1, | |
| "save_steps": 200, | |
| "stateful_callbacks": { | |
| "TrainerControl": { | |
| "args": { | |
| "should_epoch_stop": false, | |
| "should_evaluate": false, | |
| "should_log": false, | |
| "should_save": true, | |
| "should_training_stop": false | |
| }, | |
| "attributes": {} | |
| } | |
| }, | |
| "total_flos": 3.2517969149952e+16, | |
| "train_batch_size": 2, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |