Instructions to use Open-Orca/oo-phi-1_5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Open-Orca/oo-phi-1_5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Open-Orca/oo-phi-1_5", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Open-Orca/oo-phi-1_5", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Open-Orca/oo-phi-1_5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Open-Orca/oo-phi-1_5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Open-Orca/oo-phi-1_5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Open-Orca/oo-phi-1_5
- SGLang
How to use Open-Orca/oo-phi-1_5 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 "Open-Orca/oo-phi-1_5" \ --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": "Open-Orca/oo-phi-1_5", "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 "Open-Orca/oo-phi-1_5" \ --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": "Open-Orca/oo-phi-1_5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Open-Orca/oo-phi-1_5 with Docker Model Runner:
docker model run hf.co/Open-Orca/oo-phi-1_5
Create configs/phi-oo.yml
Browse files- configs/phi-oo.yml +83 -0
configs/phi-oo.yml
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base_model: microsoft/phi-1_5
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base_model_config: microsoft/phi-1_5
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model_type: AutoModelForCausalLM
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tokenizer_type: AutoTokenizer
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is_llama_derived_model: false
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trust_remote_code: true
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load_in_8bit: false
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load_in_4bit: false
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strict: false
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datasets:
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- path: openaccess-ai-collective/oo-gpt4-filtered
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type: alpaca_w_system.load_open_orca_chatml
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data_files:
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- 1M-GPT4-Augmented-filtered-gt10.parquet
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.01
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output_dir: ./phi-oo-out
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hub_model_id: Open-Orca/oo-phi-1_5
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sequence_len: 2048
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sample_packing: false
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pad_to_sequence_len:
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adapter:
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lora_model_dir:
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lora_r:
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lora_alpha:
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lora_dropout:
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lora_target_linear:
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lora_fan_in_fan_out:
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wandb_project: phi-oo
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wandb_entity: open-orca
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wandb_watch:
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wandb_run_id:
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wandb_log_model:
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gradient_accumulation_steps: 1
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micro_batch_size: 3
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num_epochs: 5
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optimizer: adamw_torch
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adam_beta2: 0.95
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adam_epsilon: 0.00001
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max_grad_norm: 1.0
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lr_scheduler: cosine
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learning_rate: 0.000003
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train_on_inputs: false
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group_by_length: true
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bf16: true
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fp16: false
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tf32: true
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gradient_checkpointing:
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early_stopping_patience:
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resume_from_checkpoint:
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local_rank:
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logging_steps: 1
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xformers_attention:
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flash_attention:
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warmup_steps: 1000
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eval_steps: 0.02
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save_steps: 0.10
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do_bench_eval: true
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bench_dataset: "pharaouk/dharma-1/dharma_1_full.json"
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debug:
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deepspeed:
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weight_decay: 0.1
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fsdp:
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fsdp_config:
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resize_token_embeddings_to_32x: true
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special_tokens:
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bos_token: "<|endoftext|>"
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eos_token: "<|im_end|>"
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unk_token: "<|endoftext|>"
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pad_token: "<|endoftext|>"
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tokens:
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- "<|im_start|>"
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- "<|im_end|>"
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