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Model: llmware/bling-cerebras-1.3b-0.1
Source: Original Platform

README.md

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+---
+license: apache-2.0  
+inference: false  
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+BLING-cerebras-1.3b-0.1 is part of the BLING ("Best Little Instruction-following No-GPU-required") model series, with instruct training on top of the cerebras/Cerebras-GPT-1.3B base.
+
+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.
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+- **Developed by:** llmware
+- **Model type:** Instruct-trained GPT decoder
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** cerebras/Cerebras-GPT-1.3B
+
+## 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.
+
+
+## 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-cerebras-1.3b-0.1")  
+    model = AutoModelForCausalLM.from_pretrained("llmware/bling-cerebras-1.3b-0.1")  
+
+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>\:"
+
+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)  
+
+   
+## Citation [optional]
+
+This BLING model is built on top of a Cerebras base GPT trained model - for more information about the Cerebras GPT models, please see the following paper:
+
+{
+Title:  Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster
+Authors: Nolan Dey, Gurpreet Gosal, Zhiming (Charles) Chen, Hemant Khachane, William Marshall, Ribhu Pathria, Marvin Tom, Joe Hestness
+Publication:  April 6, 2023
+}
+
+
+## Model Card Contact
+
+Darren Oberst & llmware team
+