dragon-yi-6b-v0 / README.md
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---
license: other
license_link: https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE
license_name: yi-license
model_creator: 01-ai
model_name: Yi 6B
model_type: yi
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
dragon-yi-6b-v0 part of the dRAGon ("Delivering RAG On ...") model series, RAG-instruct trained on top of a Yi-6B base model.
DRAGON models have been fine-tuned with the specific objective of fact-based question-answering over complex business and legal documents with an emphasis on reducing hallucinations and providing short, clear answers for workflow automation.
### Benchmark Tests
Evaluated against the benchmark test: [RAG-Instruct-Benchmark-Tester](https://www.huggingface.co/datasets/llmware/rag_instruct_benchmark_tester)
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.
--**Accuracy Score**: **99.5** correct out of 100
--Not Found Classification: 90.0%
--Boolean: 87.5%
--Math/Logic: 77.5%
--Complex Questions (1-5): 4 (Above Average)
--Summarization Quality (1-5): 4 (Above Average)
--Hallucinations: No hallucinations observed in test runs.
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:** Yi
- **Language(s) (NLP):** English
- **License:** Yi License [Link](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE)
- **Finetuned from model:** Yi-6B
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
DRAGON is designed for enterprise automation use cases, especially in knowledge-intensive industries, such as financial services,
legal and regulatory industries with complex information sources.
DRAGON models have been trained for common RAG scenarios, specifically: question-answering, key-value extraction, and basic summarization as the core instruction types
without the need for a lot of complex instruction verbiage - provide a text passage context, ask questions, and get clear fact-based responses.
This model is licensed according to the terms of the license of the base model, Yi-6B, at this [link](https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE).
## 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:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("dragon-yi-6b-v0")
model = AutoModelForCausalLM.from_pretrained("dragon-yi-6b-v0")
```
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:
```python
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:
```python
my_prompt = {{text_passage}} + "\n" + {{question/instruction}}
```
If you are using a HuggingFace generation script:
```python
# prepare prompt packaging used in fine-tuning process
new_prompt = "<human>: " + entries["context"] + "\n" + entries["query"] + "\n" + "<bot>:"
inputs = tokenizer(new_prompt, return_tensors="pt")
start_of_output = len(inputs.input_ids[0])
# temperature: set at 0.3 for consistency of output
# max_new_tokens: set at 100 - may prematurely stop a few of the summaries
outputs = model.generate(
inputs.input_ids.to(device),
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
do_sample=True,
temperature=0.3,
max_new_tokens=100,
)
output_only = tokenizer.decode(outputs[0][start_of_output:],skip_special_tokens=True)
```
## Model Card Contact
Darren Oberst & llmware team