Instructions to use cyberagent/open-calm-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cyberagent/open-calm-3b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="cyberagent/open-calm-3b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("cyberagent/open-calm-3b") model = AutoModelForCausalLM.from_pretrained("cyberagent/open-calm-3b") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use cyberagent/open-calm-3b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cyberagent/open-calm-3b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cyberagent/open-calm-3b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/cyberagent/open-calm-3b
- SGLang
How to use cyberagent/open-calm-3b 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 "cyberagent/open-calm-3b" \ --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": "cyberagent/open-calm-3b", "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 "cyberagent/open-calm-3b" \ --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": "cyberagent/open-calm-3b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use cyberagent/open-calm-3b with Docker Model Runner:
docker model run hf.co/cyberagent/open-calm-3b
| license: cc-by-sa-4.0 | |
| datasets: | |
| - wikipedia | |
| - cc100 | |
| - mc4 | |
| language: | |
| - ja | |
| tags: | |
| - japanese | |
| - causal-lm | |
| inference: false | |
| # OpenCALM-3B | |
| ## Model Description | |
| OpenCALM is a suite of decoder-only language models pre-trained on Japanese datasets, developed by CyberAgent, Inc. | |
| ## Usage | |
| ```python | |
| import torch | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model = AutoModelForCausalLM.from_pretrained("cyberagent/open-calm-3b", device_map="auto", torch_dtype=torch.float16) | |
| tokenizer = AutoTokenizer.from_pretrained("cyberagent/open-calm-3b") | |
| inputs = tokenizer("AIによって私達の暮らしは、", return_tensors="pt").to(model.device) | |
| with torch.no_grad(): | |
| tokens = model.generate( | |
| **inputs, | |
| max_new_tokens=64, | |
| do_sample=True, | |
| temperature=0.7, | |
| top_p=0.9, | |
| repetition_penalty=1.05, | |
| pad_token_id=tokenizer.pad_token_id, | |
| ) | |
| output = tokenizer.decode(tokens[0], skip_special_tokens=True) | |
| print(output) | |
| ``` | |
| ## Model Details | |
| |Model|Params|Layers|Dim|Heads|Dev ppl| | |
| |:---:|:---: |:---:|:---:|:---:|:---:| | |
| |[cyberagent/open-calm-small](https://huggingface.co/cyberagent/open-calm-small)|160M|12|768|12|19.7| | |
| |[cyberagent/open-calm-medium](https://huggingface.co/cyberagent/open-calm-medium)|400M|24|1024|16|13.8| | |
| |[cyberagent/open-calm-large](https://huggingface.co/cyberagent/open-calm-large)|830M|24|1536|16|11.3| | |
| |[cyberagent/open-calm-1b](https://huggingface.co/cyberagent/open-calm-1b)|1.4B|24|2048|16|10.3| | |
| |[cyberagent/open-calm-3b](https://huggingface.co/cyberagent/open-calm-3b)|2.7B|32|2560|32|9.7| | |
| |[cyberagent/open-calm-7b](https://huggingface.co/cyberagent/open-calm-7b)|6.8B|32|4096|32|8.2| | |
| * **Developed by**: [CyberAgent, Inc.](https://www.cyberagent.co.jp/) | |
| * **Model type**: Transformer-based Language Model | |
| * **Language**: Japanese | |
| * **Library**: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) | |
| * **License**: OpenCALM is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License ([CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)). When using this model, please provide appropriate credit to CyberAgent, Inc. | |
| * Example (en): This model is a fine-tuned version of OpenCALM-XX developed by CyberAgent, Inc. The original model is released under the CC BY-SA 4.0 license, and this model is also released under the same CC BY-SA 4.0 license. For more information, please visit: https://creativecommons.org/licenses/by-sa/4.0/ | |
| * Example (ja): 本モデルは、株式会社サイバーエージェントによるOpenCALM-XXをファインチューニングしたものです。元のモデルはCC BY-SA 4.0ライセンスのもとで公開されており、本モデルも同じくCC BY-SA 4.0ライセンスで公開します。詳しくはこちらをご覧ください: https://creativecommons.org/licenses/by-sa/4.0/ | |
| ## Training Dataset | |
| * Wikipedia (ja) | |
| * Common Crawl (ja) | |
| ## Author | |
| [Ryosuke Ishigami](https://huggingface.co/rishigami) | |
| ## Citations | |
| ```bibtext | |
| @software{gpt-neox-library, | |
| title = {{GPT-NeoX: Large Scale Autoregressive Language Modeling in PyTorch}}, | |
| author = {Andonian, Alex and Anthony, Quentin and Biderman, Stella and Black, Sid and Gali, Preetham and Gao, Leo and Hallahan, Eric and Levy-Kramer, Josh and Leahy, Connor and Nestler, Lucas and Parker, Kip and Pieler, Michael and Purohit, Shivanshu and Songz, Tri and Phil, Wang and Weinbach, Samuel}, | |
| url = {https://www.github.com/eleutherai/gpt-neox}, | |
| doi = {10.5281/zenodo.5879544}, | |
| month = {8}, | |
| year = {2021}, | |
| version = {0.0.1}, | |
| } | |
| ``` |