Instructions to use quantumaikr/falcon-180B-WizardLM_Orca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use quantumaikr/falcon-180B-WizardLM_Orca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="quantumaikr/falcon-180B-WizardLM_Orca")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("quantumaikr/falcon-180B-WizardLM_Orca") model = AutoModelForCausalLM.from_pretrained("quantumaikr/falcon-180B-WizardLM_Orca", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use quantumaikr/falcon-180B-WizardLM_Orca with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "quantumaikr/falcon-180B-WizardLM_Orca" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "quantumaikr/falcon-180B-WizardLM_Orca", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/quantumaikr/falcon-180B-WizardLM_Orca
- SGLang
How to use quantumaikr/falcon-180B-WizardLM_Orca 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 "quantumaikr/falcon-180B-WizardLM_Orca" \ --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": "quantumaikr/falcon-180B-WizardLM_Orca", "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 "quantumaikr/falcon-180B-WizardLM_Orca" \ --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": "quantumaikr/falcon-180B-WizardLM_Orca", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use quantumaikr/falcon-180B-WizardLM_Orca with Docker Model Runner:
docker model run hf.co/quantumaikr/falcon-180B-WizardLM_Orca
Commit ยท
5c3e594
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Parent(s): 219b02b
Create README.md
Browse files
README.md
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---
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datasets:
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- tiiuae/falcon-refinedweb
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- pankajmathur/WizardLM_Orca
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language:
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- en
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- de
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- es
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- fr
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inference: false
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---
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# ๐ฐ๐ท quantumaikr/falcon-180B-WizardLM_Orca
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**quantumaikr/falcon-180B-WizardLM_Orca is a 180B parameters causal decoder-only model built by [quantumaikr](https://www.quantumai.kr) based on [Falcon-180B-chat](https://huggingface.co/tiiuae/falcon-180B-chat)**
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## How to Get Started with the Model
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To run inference with the model in full `bfloat16` precision you need approximately 8xA100 80GB or equivalent.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import transformers
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import torch
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model = "quantumaikr/falcon-180B-WizardLM_Orca"
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tokenizer = AutoTokenizer.from_pretrained(model)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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tokenizer=tokenizer,
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torch_dtype=torch.bfloat16,
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trust_remote_code=True,
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device_map="auto",
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)
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sequences = pipeline(
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"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
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max_length=200,
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do_sample=True,
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top_k=10,
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num_return_sequences=1,
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eos_token_id=tokenizer.eos_token_id,
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)
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for seq in sequences:
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print(f"Result: {seq['generated_text']}")
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```
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## Contact
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๐ฐ๐ท www.quantumai.kr
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๐ฐ๐ท [email protected] [์ด๊ฑฐ๋์ธ์ด๋ชจ๋ธ ๊ธฐ์ ๋์
๋ฌธ์ํ์]
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