---
license: llama3.1
inference: false
---

# DRAGON-LLAMA-3.1-GGUF

<!-- Provide a quick summary of what the model is/does. -->

dragon-llama-3.1-gguf is RAG-instruct trained on top of a Llama-3.1 base model.

### Benchmark Tests  

Evaluated against the benchmark test:   [RAG-Instruct-Benchmark-Tester](https://www.huggingface.co/datasets/llmware/rag_instruct_benchmark_tester)  
1 Test Run (temperature=0.0, sample=False) 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.  

--**Accuracy Score**:  **94.0** correct out of 100  
--Not Found Classification:  70.0%  
--Boolean:  90.0%  
--Math/Logic:  72.5%  
--Complex Questions (1-5):  4 (Above Average - table-reading, causal)  
--Summarization Quality (1-5):  4 (Above Average)  
--Hallucinations:  No hallucinations but a few instances of drawing on 'background' knowledge.  

For test run results (and good indicator of target use cases), please see the files ("core_rag_test" and "answer_sheet" in this repo).

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** llmware
- **Model type:** Phi-2B
- **Language(s) (NLP):** English
- **License:** Llama-3.1 Community License
- **Finetuned from model:** Llama-3.1-Base



## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

Any model can provide inaccurate or incomplete information, and should be used in conjunction with appropriate safeguards and fact-checking mechanisms.


## How to Get Started with the Model

The fastest way to get started with BLING is through direct import in transformers:

    from transformers import AutoTokenizer, AutoModelForCausalLM  
    tokenizer = AutoTokenizer.from_pretrained("dragon-llama-3.1-gguf", trust_remote_code=True)  
    model = AutoModelForCausalLM.from_pretrained("dragon-llama-3.1-gguf", trust_remote_code=True)  

Please refer to the generation_test .py files in the Files repository, which includes 200 samples and script to test the model.  The **generation_test_llmware_script.py** includes built-in llmware capabilities for fact-checking, as well as easy integration with document parsing and actual retrieval to swap out the test set for RAG workflow consisting of business documents.  

The dRAGon model was fine-tuned with a simple "\<human> and \<bot> wrapper", so to get the best results, wrap inference entries as:

    full_prompt = "<human>: " + my_prompt + "\n" + "<bot>:"

The BLING model was fine-tuned with closed-context samples, which assume generally that the prompt consists of two sub-parts:

1.  Text Passage Context, and
2.  Specific question or instruction based on the text passage

To get the best results, package "my_prompt" as follows:

    my_prompt = {{text_passage}} + "\n" + {{question/instruction}}




## Model Card Contact

Darren Oberst & llmware team