llmware
/

slim-summary-phi-3

@@ -1,118 +1,33 @@
 ---
-license: apache-2.0
-inference: false
 ---
-# dragon-phi-3-answer-tool
-<!-- Provide a quick summary of what the model is/does. -->
-dragon-phi-3-answer-tool is part of the DRAGON ("Delivering RAG On ...") model series, RAG-instruct trained on top of a Microsoft Phi-3 base model.
-DRAGON models are fine-tuned with high-quality custom instruct datasets, designed for production use in RAG scenarios.
-### 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**:  **100.0** 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:** Dragon
-- **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("bling-phi-2-v0", trust_remote_code=True)
-    model = AutoModelForCausalLM.from_pretrained("bling-phi-2-v0", 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}}
-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

 ---
+license: apache-2.0
+inference: false
+tags: [green, p1, llmware-fx, ov, emerald]
 ---
+# slim-summary-tiny-ov
+**slim-summary-tiny-ov** is a specialized function calling model that summarizes a given text and generates as output a Python list of summary points.
+This is an OpenVino int4 quantized version of slim-summary-tiny, providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU.
 ### Model Description
+- **Developed by:** llmware
+- **Model type:** tinyllama
+- **Parameters:** 1.1 billion
+- **Model Parent:** llmware/slim-summary-tiny
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Uses:** Summary bulletpoints extracted from complex business documents
+- **RAG Benchmark Accuracy Score:** NA
+- **Quantization:** int4
+## Model Card Contact
+[llmware on github](https://www.github.com/llmware-ai/llmware)
+[llmware on hf](https://www.huggingface.co/llmware)
+[llmware website](https://www.llmware.ai)