83 lines
1.9 KiB
Markdown
83 lines
1.9 KiB
Markdown
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---
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datasets:
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- cerebras/SlimPajama-627B
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- HuggingFaceH4/ultrachat_200k
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- bigcode/starcoderdata
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- HuggingFaceH4/ultrafeedback_binarized
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- OEvortex/vortex-mini
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- Open-Orca/OpenOrca
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language:
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- en
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metrics:
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- speed
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library_name: transformers
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tags:
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- Text-Generation
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- Transformers
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- HelpingAI
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license: other
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license_name: hsul
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license_link: https://huggingface.co/OEvortex/vortex-3b/raw/main/LICENSE.md
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widget:
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- text: |
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<|system|>
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You are a chatbot who can be a teacher!</s>
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<|user|>
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Explain me working of AI .</s>
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<|assistant|>
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---
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🌟 **HelpingAI-Lite-1.5T Model Card** 🌟
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📊 **Datasets used:**
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- cerebras/SlimPajama-627B
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- HuggingFaceH4/ultrachat_200k
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- bigcode/starcoderdata
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- HuggingFaceH4/ultrafeedback_binarized
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- OEvortex/vortex-mini
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- Open-Orca/OpenOrca
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🗣️ **Language:**
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- English (en)
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🔒 **License:**
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HelpingAI Simplified Universal License (HSUL)
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🧠 **Model Overview:**
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HelpingAI-Lite-1.5T is an advanced version of the HelpingAI-Lite model, trained on a vast corpus of 1.5 trillion tokens. This extensive training data enables the model to provide precise and insightful responses, particularly for coding tasks.
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🔧 **Usage Example:**
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```python
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from transformers import pipeline
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from accelerate import Accelerator
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# Initialize the accelerator
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accelerator = Accelerator()
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# Initialize the pipeline
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pipe = pipeline("text-generation", model="OEvortex/HelpingAI-Lite-1.5T", device=accelerator.device)
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# Define the messages
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messages = [
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{
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"role": "system",
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"content": "You are a chatbot who can be a teacher",
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},
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{
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"role": "user",
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"content": "Explain me working of AI.",
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},
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]
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# Prepare the prompt
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prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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# Generate predictions
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outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
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# Print the generated text
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print(outputs[0]["generated_text"])
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```
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