llmware
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bling-1b-0.1

Text Generation

text-generation-inference

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doberst commited on Oct 23, 2023

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@@ -49,15 +49,6 @@ The first BLING models have been trained for common RAG scenarios, specifically:
 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. -->
@@ -76,7 +67,7 @@ model = AutoModelForCausalLM.from_pretrained("llmware/bling-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:

 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. -->
 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: