Text Generation
Transformers
TensorBoard
Safetensors
PEFT
Trained with AutoTrain
text-generation-inference
Instructions to use shafire/AgentZero with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shafire/AgentZero with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shafire/AgentZero")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shafire/AgentZero", dtype="auto") - PEFT
How to use shafire/AgentZero with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use shafire/AgentZero with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shafire/AgentZero" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shafire/AgentZero", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/shafire/AgentZero
- SGLang
How to use shafire/AgentZero 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 "shafire/AgentZero" \ --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": "shafire/AgentZero", "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 "shafire/AgentZero" \ --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": "shafire/AgentZero", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use shafire/AgentZero with Docker Model Runner:
docker model run hf.co/shafire/AgentZero
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7f1fb27 | 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 | {
"model": "google/gemma-2-9b-it",
"project_name": "AgentZero",
"data_path": "AgentZero/autotrain-data",
"train_split": "train",
"valid_split": null,
"add_eos_token": true,
"block_size": 1024,
"model_max_length": 2048,
"padding": "right",
"trainer": "sft",
"use_flash_attention_2": false,
"log": "tensorboard",
"disable_gradient_checkpointing": false,
"logging_steps": -1,
"eval_strategy": "epoch",
"save_total_limit": 1,
"auto_find_batch_size": false,
"mixed_precision": "fp16",
"lr": 3e-05,
"epochs": 3,
"batch_size": 2,
"warmup_ratio": 0.1,
"gradient_accumulation": 4,
"optimizer": "adamw_torch",
"scheduler": "linear",
"weight_decay": 0.0,
"max_grad_norm": 1.0,
"seed": 42,
"chat_template": "none",
"quantization": "int4",
"target_modules": "all-linear",
"merge_adapter": false,
"peft": true,
"lora_r": 16,
"lora_alpha": 32,
"lora_dropout": 0.05,
"model_ref": null,
"dpo_beta": 0.1,
"max_prompt_length": 128,
"max_completion_length": null,
"prompt_text_column": "autotrain_prompt",
"text_column": "autotrain_text",
"rejected_text_column": "autotrain_rejected_text",
"push_to_hub": true,
"username": "shafire",
"unsloth": false,
"distributed_backend": null
} |