Upload README.md

README.md

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 license: apache-2.0
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+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+bling-falcon-1b-0.1 is part of the BLING ("Best Little Instruction-following No-GPU-required") model series, instruct trained on top of a falcon-rw-1b base model.
+
+BLING models are fine-tuned with distilled high-quality custom instruct datasets, targeted at a specific subset of instruct tasks with 
+the objective of providing a high-quality Instruct model that is 'inference-ready' on a CPU laptop even 
+without using any advanced quantization optimizations.
+
+### **PERFORMANCE on BASIC RAG TEST DATASET** 
+							
+| Model                 |  Params (B) |	Sourcing |	GPU/CPU	 | Output Tokens | Out as % of Input | Process Time (secs) | Score (0-100) |
+| :----------           | :--------:  |  :----:  | :-----:   | :---------:   | :-------:         | :--------:          | :-------:     |
+| gpt-4	                |   <=1000	  | Closed   | Multi-GPU | 2665	         | 10.53%	         | 183.8	           |    100        |
+| gpt-3.5-turbo-instruct|	<=175	  | Closed	 | Multi-GPU |	2621	     | 11.49%	         | 62.7	               |    100        |
+| claude-instant-v1	    |   <=50	  | Closed	 | Multi-GPU |	6337	     | 26.50%	         |  154	               |    100        |
+| aib-read-gpt	        |   7	      | Closed   | GPU	     |  1964	     |  9.30%            |	114	               |     96        |
+| **bling_falcon-1b-0.1**	|   **1.3**	      | **Open**	 | **CPU**	     |  **3204**	     | **14.55%**         |  **696**               |     **77**        |
+| bling_pythia-1.4b-0.1	|   1.4	      | Open	 | CPU	     |  2589	     | 11.75%	         |  593.5	           |     65        |
+| bling_pythia-1b-0.1	|   1.0	      | Open     |	CPU	     | 2753	         | 12.49%	         |  428	               |     59        |
+| bling_cerebras-1.3b   |	1.3	      | Open     |	CPU	     | 3202	         | 20.01%	         |  690.1	           |     52        |
+| bling_pythia_410m	    |  0.41	      |  NA	     |  CPU	     |  2349	     |  10.66%	         |  189	               |     36        |
+| bling_cerebras_590m	|  0.59	      |  NA	     |  CPU	     |  4407	     |   20.01%	         |  400.8	           |     30        |  
+
+For more details on this evaluation, please see the dataset: **llmware/rag_instruct_test_dataset_0.1** and [BLOG](https://medium.com/@darrenoberst/evaluating-llm-performance-in-rag-instruct-use-cases-083dc272a31d)  
+
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** llmware
+- **Model type:** GPTNeoX instruct-trained decoder
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** tiiuae/falcon-rw-1b
+
+## 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 Instruct models that can run on a laptop for local testing.  We have found it extremely useful when building a
+   proof-of-concept, or working with sensitive enterprise data that must be closely guarded, especially in RAG use cases.
+
+2.  Push the state of the art for smaller Instruct-following models in the sub-7B parameter range, especially 1B-3B, as single-purpose
+    automation tools for specific tasks through targeted fine-tuning datasets and focused "instruction" tasks.
+
+
+### 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.  Rather than try to be "all things to all people," BLING models try to focus on a narrower set of Instructions more suitable to a ~1B parameter GPT model.
+
+BLING is ideal for rapid prototyping, testing, and the ability to perform an end-to-end workflow locally on a laptop without
+having to send sensitive information over an Internet-based API.
+
+The first 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.
+
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+1.  BLING is not designed for 'chat-bot' or 'consumer-oriented' applications. 
+
+2.  BLING is not optimal for most production applications, other than simple and highly specific use cases.
+
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+BLING has not been designed for end consumer-oriented applications, and there has not been any focus in training on safeguards to mitigate potential bias.  We would strongly discourage any use of BLING for any 'chatbot' use case.
+
+
+## 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-falcon-1b-0.1")  
+model = AutoModelForCausalLM.from_pretrained("llmware/bling-falcon-1b-0.1")  
+
+
+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>\: "
+
+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}}
+
+
+## Citation [optional]
+
+This BLING model was built on top of a Falcon model base - for more information about the Falcon model, please see the paper referenced below:
+
+@article{refinedweb,
+  title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
+  author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
+  journal={arXiv preprint arXiv:2306.01116},
+  eprint={2306.01116},
+  eprinttype = {arXiv},
+  url={https://arxiv.org/abs/2306.01116},
+  year={2023}
+}
+
+
+## Model Card Contact
+
+Darren Oberst & llmware team
+
+Please reach out anytime if you are interested in this project and would like to participate and work with us!
+
+
+