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README.md
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<!-- Provide a quick summary of what the model is/does. -->
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dragon-
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DRAGON models are fine-tuned with high-quality custom instruct datasets, designed for production quality use in RAG scenarios.
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Evaluated against the benchmark test: [RAG-Instruct-Benchmark-Tester](https://www.huggingface.co/datasets/llmware/rag_instruct_benchmark_tester)
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Average of 2 Test Runs with 1 point for correct answer, 0.5 point for partial correct or blank / NF, 0.0 points for incorrect, and -1 points for hallucinations.
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--**Accuracy Score**: **
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--Not Found Classification:
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--Boolean:
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--Math/Logic:
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--Complex Questions (1-5):
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--Summarization Quality (1-5): 4 (Coherent, extractive)
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--Hallucinations: No hallucinations observed in test runs.
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** llmware
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- **Model type:**
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- **Language(s) (NLP):** English
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- **License:** Apache 2.0
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- **Finetuned from model:**
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## Uses
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The fastest way to get started with BLING is through direct import in transformers:
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("dragon-
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model = AutoModelForCausalLM.from_pretrained("dragon-
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The BLING model was fine-tuned with a simple "\<human> and \<bot> wrapper", so to get the best results, wrap inference entries as:
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<!-- Provide a quick summary of what the model is/does. -->
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dragon-red-pajamas-7b-0.1 part of the dRAGon ("Delivering RAG On Private Cloud") model series, RAG-instruct trained on top of a Red-Pajamas-INCITE-7B base model.
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DRAGON models are fine-tuned with high-quality custom instruct datasets, designed for production quality use in RAG scenarios.
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Evaluated against the benchmark test: [RAG-Instruct-Benchmark-Tester](https://www.huggingface.co/datasets/llmware/rag_instruct_benchmark_tester)
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Average of 2 Test Runs with 1 point for correct answer, 0.5 point for partial correct or blank / NF, 0.0 points for incorrect, and -1 points for hallucinations.
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--**Accuracy Score**: **92.75** correct out of 100
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--Not Found Classification: 25.0%
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--Boolean: 76.0%
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--Math/Logic: 43.0%
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--Complex Questions (1-5): 3 (Low-Medium)
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--Summarization Quality (1-5): 4 (Coherent, extractive)
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--Hallucinations: No hallucinations observed in test runs.
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** llmware
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- **Model type:** Red-Pajamas-INCITE-7B
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- **Language(s) (NLP):** English
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- **License:** Apache 2.0
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- **Finetuned from model:** Red-Pajamas-INCITE-7B
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## Uses
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The fastest way to get started with BLING is through direct import in transformers:
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("dragon-red-pajamas-7b-0.1")
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model = AutoModelForCausalLM.from_pretrained("dragon-red-pajamas-7b-0.1")
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The BLING model was fine-tuned with a simple "\<human> and \<bot> wrapper", so to get the best results, wrap inference entries as:
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