|
--- |
|
license: apache-2.0 |
|
inference: false |
|
--- |
|
|
|
# bling-phi-3 |
|
|
|
<!-- Provide a quick summary of what the model is/does. --> |
|
|
|
bling-phi-3 is part of the BLING ("Best Little Instruct No-GPU") model series, RAG-instruct trained on top of a Microsoft Phi-3 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**: **99.5** correct out of 100 |
|
--Not Found Classification: 95.0% |
|
--Boolean: 97.5% |
|
--Math/Logic: 80.0% |
|
--Complex Questions (1-5): 4 (Above Average - multiple-choice, causal) |
|
--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:** bling |
|
- **Language(s) (NLP):** English |
|
- **License:** Apache 2.0 |
|
- **Finetuned from model:** Microsoft Phi-3 |
|
|
|
## Uses |
|
|
|
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
|
|
|
The intended use of BLING models is two-fold: |
|
|
|
1. Provide high-quality RAG-Instruct models designed for fact-based, no "hallucination" question-answering in connection with an enterprise RAG workflow. |
|
|
|
2. BLING models are fine-tuned on top of leading base foundation models, generally in the 1-3B+ range, and purposefully rolled-out across multiple base models to provide choices and "drop-in" replacements for RAG specific use cases. |
|
|
|
|
|
### Direct Use |
|
|
|
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
|
|
|
BLING is designed for enterprise automation use cases, especially in knowledge-intensive industries, such as financial services, |
|
legal and regulatory industries with complex information sources. |
|
|
|
BLING 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. |
|
|
|
|
|
## 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("llmware/bling-phi-3", trust_remote_code=True) |
|
model = AutoModelForCausalLM.from_pretrained("llmware/bling-phi-3", 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 BLING 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>:" |
|
|
|
(As an aside, we intended to retire "human-bot" and tried several variations of the new Microsoft Phi-3 prompt template and ultimately had slightly better results with the very simple "human-bot" separators, so we opted to keep them.) |
|
|
|
|
|
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}} |
|
|
|
|
|
If you are using a HuggingFace generation script: |
|
|
|
# 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 |
|
|
