H2o Danube 1.8B Sft by h2oai

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  Arxiv:2401.16818   Autotrain compatible   Conversational Dataset:huggingfaceh4/ultracha...   Dataset:meta-math/metamathqa   Dataset:open-orca/openorca   Dataset:openassistant/oasst2   En   Endpoints compatible   Gpt   H2o-llmstudio   Mistral   Region:us   Safetensors

H2o Danube 1.8B Sft Benchmarks

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H2o Danube 1.8B Sft Parameters and Internals

Model Type 
chat, fine-tuned
Use Cases 
Areas:
Research, Commercial applications
Limitations:
Incorrect, nonsensical or irrelevant responses
Considerations:
Users should ensure ethical use and be aware of potential biases in the generated outputs.
Additional Notes 
The model architecture details and usage guidelines are provided in the model card and technical report.
Training Details 
Data Sources:
Open-Orca/OpenOrca, OpenAssistant/oasst2, HuggingFaceH4/ultrachat_200k, meta-math/MetaMathQA
Methodology:
The model was trained using H2O LLM Studio with sliding window attention from mistral.
Context Length:
16384
Model Architecture:
MistralForCausalLM incorporating Llama 2 architecture with sliding window attention.
Input Output 
Performance Tips:
Quantization and sharding are supported in the transformers library for efficient usage.
LLM NameH2o Danube 1.8B Sft
Repository ๐Ÿค—https://huggingface.co/h2oai/h2o-danube-1.8b-sft 
Model Size1.8b
Required VRAM3.7 GB
Updated2024-12-02
Maintainerh2oai
Model Typemistral
Model Files  3.7 GB
Supported Languagesen
Model ArchitectureMistralForCausalLM
Licenseapache-2.0
Context Length16384
Model Max Length16384
Transformers Version4.36.1
Tokenizer ClassLlamaTokenizer
Padding Token<unk>
Vocabulary Size32000
Torch Data Typebfloat16
H2o Danube 1.8B Sft (h2oai/h2o-danube-1.8b-sft)

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Original data from HuggingFace, OpenCompass and various public git repos.
Release v20241124