Instructions to use dancil/dejavu-othello-intercode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dancil/dejavu-othello-intercode with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Jordansky/augmented-cb63c157cc726c7e") model = PeftModel.from_pretrained(base_model, "dancil/dejavu-othello-intercode") - Transformers
How to use dancil/dejavu-othello-intercode with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dancil/dejavu-othello-intercode") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("dancil/dejavu-othello-intercode", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use dancil/dejavu-othello-intercode with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dancil/dejavu-othello-intercode" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dancil/dejavu-othello-intercode", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/dancil/dejavu-othello-intercode
- SGLang
How to use dancil/dejavu-othello-intercode 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 "dancil/dejavu-othello-intercode" \ --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": "dancil/dejavu-othello-intercode", "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 "dancil/dejavu-othello-intercode" \ --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": "dancil/dejavu-othello-intercode", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use dancil/dejavu-othello-intercode with Docker Model Runner:
docker model run hf.co/dancil/dejavu-othello-intercode
| { | |
| "best_global_step": null, | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 0.5235602094240838, | |
| "eval_steps": 500, | |
| "global_step": 100, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.02617801047120419, | |
| "grad_norm": 1.4316072463989258, | |
| "learning_rate": 6.363636363636363e-06, | |
| "loss": 1.0183, | |
| "mean_token_accuracy": 0.8535788297653198, | |
| "num_tokens": 367251.0, | |
| "step": 5 | |
| }, | |
| { | |
| "epoch": 0.05235602094240838, | |
| "grad_norm": 0.3439352810382843, | |
| "learning_rate": 1.4318181818181818e-05, | |
| "loss": 0.3552, | |
| "mean_token_accuracy": 0.9084522366523743, | |
| "num_tokens": 736944.0, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 0.07853403141361257, | |
| "grad_norm": 0.4000185430049896, | |
| "learning_rate": 2.227272727272727e-05, | |
| "loss": 0.3266, | |
| "mean_token_accuracy": 0.9090922474861145, | |
| "num_tokens": 1103710.0, | |
| "step": 15 | |
| }, | |
| { | |
| "epoch": 0.10471204188481675, | |
| "grad_norm": 0.4231952726840973, | |
| "learning_rate": 3.0227272727272725e-05, | |
| "loss": 0.3551, | |
| "mean_token_accuracy": 0.9081164598464966, | |
| "num_tokens": 1471709.0, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 0.13089005235602094, | |
| "grad_norm": 0.31727662682533264, | |
| "learning_rate": 3.499979493322583e-05, | |
| "loss": 0.3665, | |
| "mean_token_accuracy": 0.9083151340484619, | |
| "num_tokens": 1841439.0, | |
| "step": 25 | |
| }, | |
| { | |
| "epoch": 0.15706806282722513, | |
| "grad_norm": 0.28530699014663696, | |
| "learning_rate": 3.499748800560744e-05, | |
| "loss": 0.3446, | |
| "mean_token_accuracy": 0.9064475655555725, | |
| "num_tokens": 2207080.0, | |
| "step": 30 | |
| }, | |
| { | |
| "epoch": 0.18324607329842932, | |
| "grad_norm": 0.3164561986923218, | |
| "learning_rate": 3.499261826894553e-05, | |
| "loss": 0.3102, | |
| "mean_token_accuracy": 0.9147708892822266, | |
| "num_tokens": 2577288.0, | |
| "step": 35 | |
| }, | |
| { | |
| "epoch": 0.2094240837696335, | |
| "grad_norm": 0.48501917719841003, | |
| "learning_rate": 3.498518667429489e-05, | |
| "loss": 0.3125, | |
| "mean_token_accuracy": 0.9136863827705384, | |
| "num_tokens": 2943463.0, | |
| "step": 40 | |
| }, | |
| { | |
| "epoch": 0.2356020942408377, | |
| "grad_norm": 0.39581820368766785, | |
| "learning_rate": 3.497519467303868e-05, | |
| "loss": 0.3328, | |
| "mean_token_accuracy": 0.9117220759391784, | |
| "num_tokens": 3319962.0, | |
| "step": 45 | |
| }, | |
| { | |
| "epoch": 0.2617801047120419, | |
| "grad_norm": 0.34402891993522644, | |
| "learning_rate": 3.4962644216604936e-05, | |
| "loss": 0.3148, | |
| "mean_token_accuracy": 0.912682318687439, | |
| "num_tokens": 3690158.0, | |
| "step": 50 | |
| }, | |
| { | |
| "epoch": 0.2879581151832461, | |
| "grad_norm": 0.29138538241386414, | |
| "learning_rate": 3.49475377560855e-05, | |
| "loss": 0.3115, | |
| "mean_token_accuracy": 0.9203069567680359, | |
| "num_tokens": 4062060.0, | |
| "step": 55 | |
| }, | |
| { | |
| "epoch": 0.31413612565445026, | |
| "grad_norm": 0.2792457640171051, | |
| "learning_rate": 3.492987824175731e-05, | |
| "loss": 0.3015, | |
| "mean_token_accuracy": 0.9141550898551941, | |
| "num_tokens": 4436254.0, | |
| "step": 60 | |
| }, | |
| { | |
| "epoch": 0.3403141361256545, | |
| "grad_norm": 0.30358779430389404, | |
| "learning_rate": 3.4909669122506196e-05, | |
| "loss": 0.2302, | |
| "mean_token_accuracy": 0.9322636842727661, | |
| "num_tokens": 4811698.0, | |
| "step": 65 | |
| }, | |
| { | |
| "epoch": 0.36649214659685864, | |
| "grad_norm": 0.2963319718837738, | |
| "learning_rate": 3.4886914345153334e-05, | |
| "loss": 0.2878, | |
| "mean_token_accuracy": 0.9199236869812012, | |
| "num_tokens": 5182959.0, | |
| "step": 70 | |
| }, | |
| { | |
| "epoch": 0.39267015706806285, | |
| "grad_norm": 0.3031884431838989, | |
| "learning_rate": 3.486161835368445e-05, | |
| "loss": 0.2966, | |
| "mean_token_accuracy": 0.9157989859580994, | |
| "num_tokens": 5555766.0, | |
| "step": 75 | |
| }, | |
| { | |
| "epoch": 0.418848167539267, | |
| "grad_norm": 0.2854786217212677, | |
| "learning_rate": 3.4833786088381864e-05, | |
| "loss": 0.3078, | |
| "mean_token_accuracy": 0.9176850080490112, | |
| "num_tokens": 5923597.0, | |
| "step": 80 | |
| }, | |
| { | |
| "epoch": 0.44502617801047123, | |
| "grad_norm": 0.26075834035873413, | |
| "learning_rate": 3.480342298485971e-05, | |
| "loss": 0.2726, | |
| "mean_token_accuracy": 0.9211544632911682, | |
| "num_tokens": 6292042.0, | |
| "step": 85 | |
| }, | |
| { | |
| "epoch": 0.4712041884816754, | |
| "grad_norm": 0.24862438440322876, | |
| "learning_rate": 3.477053497300234e-05, | |
| "loss": 0.3242, | |
| "mean_token_accuracy": 0.9118959903717041, | |
| "num_tokens": 6667493.0, | |
| "step": 90 | |
| }, | |
| { | |
| "epoch": 0.4973821989528796, | |
| "grad_norm": 0.22300361096858978, | |
| "learning_rate": 3.473512847580624e-05, | |
| "loss": 0.2891, | |
| "mean_token_accuracy": 0.9222010493278503, | |
| "num_tokens": 7030101.0, | |
| "step": 95 | |
| }, | |
| { | |
| "epoch": 0.5235602094240838, | |
| "grad_norm": 0.3619837760925293, | |
| "learning_rate": 3.4697210408125585e-05, | |
| "loss": 0.3445, | |
| "mean_token_accuracy": 0.909607207775116, | |
| "num_tokens": 7401039.0, | |
| "step": 100 | |
| } | |
| ], | |
| "logging_steps": 5, | |
| "max_steps": 1146, | |
| "num_input_tokens_seen": 0, | |
| "num_train_epochs": 6, | |
| "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": 3.2115031692804096e+17, | |
| "train_batch_size": 16, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |