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--- |
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license: apache-2.0 |
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language: |
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- ru |
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- en |
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- de |
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- es |
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- it |
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- ja |
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- vi |
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- zh |
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- fr |
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- pt |
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- id |
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- ko |
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pipeline_tag: text-generation |
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--- |
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# 🌍 Vulture-40B |
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***Vulture-40B*** is a further fine-tuned causal Decoder-only LLM built by Virtual Interactive (VILM), on top of the famous **Falcon-40B** by [TII](https://www.tii.ae). We collected a new dataset from news articles and Wikipedia's pages of **12 languages** (Total: **80GB**) and continue the pretraining process of Falcon-40B. Finally, we construct a multilingual instructional dataset following **Alpaca**'s techniques. |
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*Technical Report coming soon* 🤗 |
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## Prompt Format |
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The reccomended model usage is: |
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``` |
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A chat between a curious user and an artificial intelligence assistant. |
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USER:{user's question}<|endoftext|>ASSISTANT: |
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``` |
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# Model Details |
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## Model Description |
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- **Developed by:** [https://www.tii.ae](https://www.tii.ae) |
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- **Finetuned by:** [Virtual Interactive](https://vilm.org) |
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- **Language(s) (NLP):** English, German, Spanish, French, Portugese, Russian, Italian, Vietnamese, Indonesian, Chinese, Japanese and Korean |
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- **Training Time:** 1,800 A100 Hours |
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## Acknowledgement |
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- Thanks to **TII** for the amazing **Falcon** as the foundation model. |
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- Big thanks to **Google** for their generous Cloud credits. |
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### Out-of-Scope Use |
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Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful. |
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## Bias, Risks, and Limitations |
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Vulture-40B is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online. |
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### Recommendations |
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We recommend users of Vulture-40B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use. |
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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 4xA100 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 = "vilm/vulture-40B" |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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m = AutoModelForCausalLM.from_pretrained(model, torch_dtype=torch.bfloat16, device_map="auto" ) |
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prompt = "A chat between a curious user and an artificial intelligence assistant.\n\nUSER:Thành phố Hồ Chí Minh nằm ở đâu?<|endoftext|>ASSISTANT:" |
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda") |
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output = m.generate(input_ids=inputs["input_ids"], |
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attention_mask=inputs["attention_mask"], |
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do_sample=True, |
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temperature=0.6, |
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top_p=0.9, |
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max_new_tokens=50,) |
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output = output[0].to("cpu") |
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print(tokenizer.decode(output)) |
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``` |
