base_model, datasets, license, language, model_creator, model_name, model_type, inference, tags, pipeline_tag, prompt_template, quantized_by

base_model	datasets	license	language	model_creator	model_name	model_type	inference	tags	pipeline_tag	prompt_template	quantized_by
h2oai/h2o-danube-1.8b-chat	HuggingFaceH4/ultrafeedback_binarized Intel/orca_dpo_pairs argilla/distilabel-math-preference-dpo Open-Orca/OpenOrca OpenAssistant/oasst2 HuggingFaceH4/ultrachat_200k meta-math/MetaMathQA	apache-2.0	en	h2oai	h2o-danube-1.8b-chat	mistral	false	gpt llm large language model h2o-llmstudio	text-generation	<|system|>{{system_message}}</s> <|prompt|>{{prompt}}</s> <|answer|>	brittlewis12

h2o-danube-1.8b-chat GGUF

Original model: h2o-danube-1.8b-chat Model creator: h2oai

This repo contains GGUF format model files for h2oai’s h2o-danube-1.8b-chat.

h2o-danube-1.8b-chat is an chat fine-tuned model by H2O.ai with 1.8 billion parameters. For details, please refer to our Technical Report. We release three versions of this model:

• h2oai/h2o-danube-1.8b-base Base model
• h2oai/h2o-danube-1.8b-sft SFT tuned
• h2oai/h2o-danube-1.8b-chat SFT + DPO tuned

We adjust the Llama 2 architecture for a total of around 1.8b parameters. We use the original Llama 2 tokenizer with a vocabulary size of 32,000 and train our model up to a context length of 16,384. We incorporate the sliding window attention from mistral with a size of 4,096.

Refer to h2o.ai’s model disclaimer for terms of use.

What is GGUF?

GGUF is a file format for representing AI models. It is the third version of the format, introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Converted using llama.cpp b2037 (1cfb537)

Prompt template:

<|system|>{{system_message}}</s>
<|prompt|>{{prompt}}</s>
<|answer|>

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• create & save Characters with custom system prompts & temperature settings
• download and experiment with any GGUF model you can find on HuggingFace!
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• powered by Metal ⚡️ & Llama.cpp, with haptics during response streaming!
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Original Model Evaluations:

Commonsense, world-knowledge and reading comprehension tested in 0-shot:

Benchmark	acc_n
ARC-easy	67.51
ARC-challenge	39.25
BoolQ	77.89
Hellaswag	67.60
OpenBookQA	39.20
PiQA	76.71
TriviaQA	36.29
Winogrande	65.35