Instructions to use AbstractPhil/beeper-rose-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AbstractPhil/beeper-rose-v4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AbstractPhil/beeper-rose-v4")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AbstractPhil/beeper-rose-v4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AbstractPhil/beeper-rose-v4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AbstractPhil/beeper-rose-v4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AbstractPhil/beeper-rose-v4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AbstractPhil/beeper-rose-v4
- SGLang
How to use AbstractPhil/beeper-rose-v4 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 "AbstractPhil/beeper-rose-v4" \ --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": "AbstractPhil/beeper-rose-v4", "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 "AbstractPhil/beeper-rose-v4" \ --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": "AbstractPhil/beeper-rose-v4", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AbstractPhil/beeper-rose-v4 with Docker Model Runner:
docker model run hf.co/AbstractPhil/beeper-rose-v4
Download config.json from AbstractPhil/beeper-rose-v4: direct link, hf CLI and curl.
- Browser
- Download file 21.3 kB
-
https://huggingface.co/AbstractPhil/beeper-rose-v4/resolve/main/config.json
- Command line
-
hf download hf://AbstractPhil/beeper-rose-v4/config.json
-
curl -L -o config.json https://huggingface.co/AbstractPhil/beeper-rose-v4/resolve/main/config.json
21.3 kB
| { | |
| "name": "Rose-Beeper", | |
| "context": 512, | |
| "vocab_size": 8192, | |
| "dim": 512, | |
| "n_layers": 6, | |
| "n_heads": 8, | |
| "mlp_ratio": 4.0, | |
| "dropout": 0.0, | |
| "resid_dropout": 0.1, | |
| "grad_checkpoint": false, | |
| "compile_model": false, | |
| "tokenizer_path": "beeper.tokenizer.json", | |
| "add_bos_eos": true, | |
| "span_corrupt_frac": 0.0, | |
| "val_ratio": 0.01, | |
| "test_ratio": 0.01, | |
| "max_rows_per_dataset": null, | |
| "dataset_cache_verbose": true, | |
| "batch_size": 128, | |
| "grad_accum_steps": 1, | |
| "epochs": 10, | |
| "lr": 0.0003, | |
| "betas": [ | |
| 0.9, | |
| 0.95 | |
| ], | |
| "weight_decay": 0.1, | |
| "warmup_steps": 500, | |
| "max_steps": null, | |
| "clip_grad": 1.0, | |
| "min_lr": 1e-06, | |
| "label_smoothing": 0.0, | |
| "mixed_precision": "bf16", | |
| "log_dir": "./runs/rose_beeper", | |
| "log_interval": 50, | |
| "ckpt_dir": "./beeper_checkpoints", | |
| "export_dir": "./beeper_export", | |
| "temperature": 0.9, | |
| "top_k": 40, | |
| "top_p": 0.9, | |
| "repetition_penalty": 1.1, | |
| "presence_penalty": 0.6, | |
| "frequency_penalty": 0.0, | |
| "hf_repo": "AbstractPhil/beeper-rose-v4", | |
| "upload_to_hub": true, | |
| "resume": true, | |
| "resume_tag": "best_model.safetensors", | |
| "resume_strict": false, | |
| "pent_level": "medium", | |
| "lambda_contrast": 0.25, | |
| "pent_min_edge": 0.5, | |
| "pent_temp": 0.1, | |
| "contrast_warmup": 800, | |
| "lambda_rose": 0.1, | |
| "rose_scale": 1.8, | |
| "lambda_geom_sep": 0.5, | |
| "geom_sep_margin": 0.9, | |
| "lambda_geom": 0.3, | |
| "lambda_geom_angle": 0.8, | |
| "lambda_geom_var": 0.3, | |
| "lambda_geom_edge": 0.3, | |
| "lambda_geom_vol": 0.6, | |
| "lambda_geom_minrel": 1.0, | |
| "geom_min_edge_rel": 0.6, | |
| "geom_vol_lower_frac": 0.85, | |
| "geom_sample_classes": 64, | |
| "geom_sample_k": 64, | |
| "seed": 1337, | |
| "corpus": [ | |
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| "path": "wikimedia/wikipedia", | |
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| "weight": 0.5, | |
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| "path": "allenai/ai2_arc", | |
| "config": "ARC-Easy", | |
| "split": "train[30%:60%]", | |
| "weight": 0.6, | |
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| { | |
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| { | |
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| "path": "HuggingFaceH4/MATH-500", | |
| "split": "test", | |
| "weight": 0.25, | |
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| { | |
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| "path": "AiresPucrs/stanford-encyclopedia-philosophy", | |
| "split": "train", | |
| "weight": 0.3, | |
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| "path": "hendrycks/ethics", | |
| "config": "commonsense", | |
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| "path": "hendrycks/ethics", | |
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| "path": "allenai/social-chemistry-101", | |
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| { | |
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| "path": "demelin/moral_stories", | |
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| { | |
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| "path": "allenai/art", | |
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| { | |
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| "path": "ariesutiono/entailment-bank-v3", | |
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| "path": "tasksource/logiqa-2.0-nli", | |
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| "capoera": { | |
| "enable": true, | |
| "topic_bins": 512, | |
| "mood_bins": 7 | |
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| "_ok_entries": [ | |
| { | |
| "name": "TinyStories", | |
| "path": "roneneldan/TinyStories", | |
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| { | |
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| { | |
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