Instructions to use QuixiAI/based-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuixiAI/based-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="QuixiAI/based-7b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("QuixiAI/based-7b") model = AutoModelForCausalLM.from_pretrained("QuixiAI/based-7b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use QuixiAI/based-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuixiAI/based-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuixiAI/based-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/QuixiAI/based-7b
- SGLang
How to use QuixiAI/based-7b 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 "QuixiAI/based-7b" \ --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": "QuixiAI/based-7b", "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 "QuixiAI/based-7b" \ --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": "QuixiAI/based-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use QuixiAI/based-7b with Docker Model Runner:
docker model run hf.co/QuixiAI/based-7b
| { | |
| "best_metric": null, | |
| "best_model_checkpoint": null, | |
| "epoch": 2.0, | |
| "global_step": 22, | |
| "is_hyper_param_search": false, | |
| "is_local_process_zero": true, | |
| "is_world_process_zero": true, | |
| "log_history": [ | |
| { | |
| "epoch": 0.09, | |
| "learning_rate": 2e-05, | |
| "loss": 2.2461, | |
| "step": 1 | |
| }, | |
| { | |
| "epoch": 0.18, | |
| "learning_rate": 1.9888308262251286e-05, | |
| "loss": 2.0273, | |
| "step": 2 | |
| }, | |
| { | |
| "epoch": 0.27, | |
| "learning_rate": 1.955572805786141e-05, | |
| "loss": 1.8828, | |
| "step": 3 | |
| }, | |
| { | |
| "epoch": 0.36, | |
| "learning_rate": 1.900968867902419e-05, | |
| "loss": 1.8164, | |
| "step": 4 | |
| }, | |
| { | |
| "epoch": 0.45, | |
| "learning_rate": 1.826238774315995e-05, | |
| "loss": 1.6875, | |
| "step": 5 | |
| }, | |
| { | |
| "epoch": 0.55, | |
| "learning_rate": 1.7330518718298263e-05, | |
| "loss": 1.707, | |
| "step": 6 | |
| }, | |
| { | |
| "epoch": 0.64, | |
| "learning_rate": 1.6234898018587336e-05, | |
| "loss": 1.8535, | |
| "step": 7 | |
| }, | |
| { | |
| "epoch": 0.73, | |
| "learning_rate": 1.5000000000000002e-05, | |
| "loss": 1.9043, | |
| "step": 8 | |
| }, | |
| { | |
| "epoch": 0.82, | |
| "learning_rate": 1.3653410243663953e-05, | |
| "loss": 1.6289, | |
| "step": 9 | |
| }, | |
| { | |
| "epoch": 0.91, | |
| "learning_rate": 1.2225209339563144e-05, | |
| "loss": 1.9473, | |
| "step": 10 | |
| }, | |
| { | |
| "epoch": 1.0, | |
| "learning_rate": 1.0747300935864245e-05, | |
| "loss": 1.1553, | |
| "step": 11 | |
| }, | |
| { | |
| "epoch": 1.09, | |
| "learning_rate": 9.252699064135759e-06, | |
| "loss": 0.7178, | |
| "step": 12 | |
| }, | |
| { | |
| "epoch": 1.18, | |
| "learning_rate": 7.774790660436857e-06, | |
| "loss": 0.7715, | |
| "step": 13 | |
| }, | |
| { | |
| "epoch": 1.27, | |
| "learning_rate": 6.34658975633605e-06, | |
| "loss": 0.8613, | |
| "step": 14 | |
| }, | |
| { | |
| "epoch": 1.36, | |
| "learning_rate": 5.000000000000003e-06, | |
| "loss": 0.7061, | |
| "step": 15 | |
| }, | |
| { | |
| "epoch": 1.45, | |
| "learning_rate": 3.7651019814126656e-06, | |
| "loss": 0.6904, | |
| "step": 16 | |
| }, | |
| { | |
| "epoch": 1.55, | |
| "learning_rate": 2.669481281701739e-06, | |
| "loss": 0.6143, | |
| "step": 17 | |
| }, | |
| { | |
| "epoch": 1.64, | |
| "learning_rate": 1.7376122568400533e-06, | |
| "loss": 0.6147, | |
| "step": 18 | |
| }, | |
| { | |
| "epoch": 1.73, | |
| "learning_rate": 9.903113209758098e-07, | |
| "loss": 0.7466, | |
| "step": 19 | |
| }, | |
| { | |
| "epoch": 1.82, | |
| "learning_rate": 4.4427194213859216e-07, | |
| "loss": 0.6118, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 1.82, | |
| "eval_loss": 2.4375, | |
| "eval_runtime": 0.6236, | |
| "eval_samples_per_second": 1.603, | |
| "eval_steps_per_second": 1.603, | |
| "step": 20 | |
| }, | |
| { | |
| "epoch": 1.91, | |
| "learning_rate": 1.1169173774871478e-07, | |
| "loss": 0.6118, | |
| "step": 21 | |
| }, | |
| { | |
| "epoch": 2.0, | |
| "learning_rate": 0.0, | |
| "loss": 0.4739, | |
| "step": 22 | |
| } | |
| ], | |
| "max_steps": 22, | |
| "num_train_epochs": 2, | |
| "total_flos": 274810798080.0, | |
| "trial_name": null, | |
| "trial_params": null | |
| } | |