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--- |
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license: mit |
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datasets: |
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- teknium/openhermes |
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language: |
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- en |
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metrics: |
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- accuracy |
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library_name: transformers |
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pipeline_tag: question-answering |
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tags: |
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- General |
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--- |
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# StableHermes-3b by cxllin |
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## Overview |
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StableHermes-3b is an advanced 3 billion parameter language model fine-tuned on the expansive OpenHermes dataset. This dataset boasts 242,000 entries primarily sourced from GPT-4 generated data, encompassing a variety of open datasets from the broader AI landscape. As an enhancement of the GPT-NeoX family, StableHermes-3b is specifically designed to provide accurate and detailed insights across a myriad of domains. |
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## Key Features |
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- **3 Billion Parameters:** State-of-the-art architecture emphasizing precision and detail. |
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- **Diverse Training Data:** Benefits from entries like GPTeacher datasets, WizardLM, Airoboros GPT-4, Camel-AI's domain expert datasets, and more. |
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- **Open Source Dataset:** OpenHermes is one of the first fine-tunes of the Hermes dataset that has an entirely open-source dataset. |
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- **Advanced Transformer Decoder Architecture:** Based on the GPT-NeoX's decoder-only language model structure. |
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## Usage |
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To leverage StableHermes-3b for generating insights or responses, you can use the following code snippet: |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("cxllin/StableHermes-3b") |
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model = AutoModelForCausalLM.from_pretrained( |
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"cxllin/StableHermes-3b", |
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trust_remote_code=True, |
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torch_dtype="auto", |
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) |
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model.cuda() |
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inputs = tokenizer("Describe the potential implications of quantum computing on the future of cybersecurity.", return_tensors="pt").to("cuda") |
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tokens = model.generate( |
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**inputs, |
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max_new_tokens=64, |
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temperature=0.75, |
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top_p=0.95, |
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do_sample=True, |
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) |
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print(tokenizer.decode(tokens[0], skip_special_tokens=True)) |
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``` |
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# Training Eval |
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