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README.md

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+---
+base_model: llmware/bling-sheared-llama-2.7b-0.1
+inference: false
+license: apache-2.0
+model_creator: llmware
+model_name: bling-sheared-llama-2.7b-0.1
+pipeline_tag: text-generation
+quantized_by: afrideva
+tags:
+- gguf
+- ggml
+- quantized
+- q2_k
+- q3_k_m
+- q4_k_m
+- q5_k_m
+- q6_k
+- q8_0
+---
+# llmware/bling-sheared-llama-2.7b-0.1-GGUF
+
+Quantized GGUF model files for [bling-sheared-llama-2.7b-0.1](https://huggingface.co/llmware/bling-sheared-llama-2.7b-0.1) from [llmware](https://huggingface.co/llmware)
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [bling-sheared-llama-2.7b-0.1.q2_k.gguf](https://huggingface.co/afrideva/bling-sheared-llama-2.7b-0.1-GGUF/resolve/main/bling-sheared-llama-2.7b-0.1.q2_k.gguf) | q2_k | 1.14 GB  |
+| [bling-sheared-llama-2.7b-0.1.q3_k_m.gguf](https://huggingface.co/afrideva/bling-sheared-llama-2.7b-0.1-GGUF/resolve/main/bling-sheared-llama-2.7b-0.1.q3_k_m.gguf) | q3_k_m | 1.33 GB  |
+| [bling-sheared-llama-2.7b-0.1.q4_k_m.gguf](https://huggingface.co/afrideva/bling-sheared-llama-2.7b-0.1-GGUF/resolve/main/bling-sheared-llama-2.7b-0.1.q4_k_m.gguf) | q4_k_m | 1.64 GB  |
+| [bling-sheared-llama-2.7b-0.1.q5_k_m.gguf](https://huggingface.co/afrideva/bling-sheared-llama-2.7b-0.1-GGUF/resolve/main/bling-sheared-llama-2.7b-0.1.q5_k_m.gguf) | q5_k_m | 1.92 GB  |
+| [bling-sheared-llama-2.7b-0.1.q6_k.gguf](https://huggingface.co/afrideva/bling-sheared-llama-2.7b-0.1-GGUF/resolve/main/bling-sheared-llama-2.7b-0.1.q6_k.gguf) | q6_k | 2.22 GB  |
+| [bling-sheared-llama-2.7b-0.1.q8_0.gguf](https://huggingface.co/afrideva/bling-sheared-llama-2.7b-0.1-GGUF/resolve/main/bling-sheared-llama-2.7b-0.1.q8_0.gguf) | q8_0 | 2.87 GB  |
+
+
+
+## Original Model Card:
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+llmware/bling-sheared-llama-2.7b-0.1 is part of the BLING ("Best Little Instruction-following No-GPU-required") model series, RAG-instruct trained on top of a Sheared-LLaMA-2.7B 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.
+
+
+### 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**:  **90.25** correct out of 100  
+--Not Found Classification:  60.0%  
+--Boolean:  80.0%  
+--Math/Logic:  50.0%  
+--Complex Questions (1-5):  2 (Low-Medium)  
+--Summarization Quality (1-5):  3 (Coherent, extractive)  
+--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:** Instruct-trained decoder
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** princeton-nlp/Sheared-LLaMA-2.7B
+
+  
+## 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 ~1-3B 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.
+
+
+## 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-sheared-llama-2.7b-0.1")  
+model = AutoModelForCausalLM.from_pretrained("llmware/bling-sheared-llama-2.7b-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 Sheared Llama model base - for more information about the Sheared Llama model, please see the paper referenced below:
+
+@article{xia2023sheared,
+   title={Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning},
+   author={Xia, Mengzhou and Gao, Tianyu, and Zeng Zhiyuan, and Chen Danqi},
+   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!