Update README.md

README.md

@@ -1,20 +1,12 @@
----
-license: other
-datasets:
-- nvidia/CantTalkAboutThis-Topic-Control-Dataset
-language:
-- en
-metrics:
-- f1
-base_model:
-- meta-llama/Llama-3.1-8B-Instruct
-pipeline_tag: text-classification
-library_name: peft
----
 # Model Overview
 ## Description:
+
 **Llama-3.1-NemoGuard-8B-Topic-Control** can be used for topical and dialogue moderation of user prompts in human-assistant interactions being designed for task-oriented dialogue agents and custom policy-based moderation. 
+
+Try out the model here: [Llama-3.1-NemoGuard-8B-Topic-Control](https://build.ngc.nvidia.com/nvidia/llama-3_1-nemoguard-8b-topic-control)
+
 Given a system instruction (also called topical instruction, i.e. specifying which topics are allowed and disallowed) and a conversation history ending with the last user prompt, the model returns a binary response that flags if the user message respects the system instruction, (i.e. message is on-topic or a distractor/off-topic). 
+
 The base large language model (LLM) is the multilingual [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) model from Meta. Llama-3.1-TopicGuard is LoRa-tuned on a topic-following dataset generated synthetically with [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1).
 This model is ready for commercial use.  <br>
 
@@ -36,6 +28,103 @@ Related paper:
 ```
   <br>
 
+## Using the Model
+
+Llama 3.1 NemoGuard 8B TopicControl performs input moderation, such as ensuring that the user prompt is consistent with rules specified as part of the system prompt.
+
+The prompt template consists of two key sections: system instruction and conversation history that includes a sequence of user prompts and LLM responses. Typically, the prompt concludes with the current user query.
+
+
+### System Instruction
+
+The system instruction part of the prompt serves as a comprehensive guideline to steer the conversation.
+This part includes the following:
+
+Core Rules
+: A set of principles to govern the interactions to ensure relevance and adherence to any specified boundaries.
+
+Persona Assignment
+: Enables the model to adopt a specific role, such as _banking assistant_. In such cases:
+
+  - Queries outside the persona's domain are treated as off-topic, by default.
+  - You can specify subcategories or topics to block within the persona's scope.
+    For example, a banking assistant could be instructed to block topics like cryptocurrency or insurance claims.
+
+These instructions are used by the topic control model to determine whether a user query aligns with the defined rules.
+
+The system prompt must end with the TOPIC_SAFETY_OUTPUT_RESTRICTION string. This ensures that the prediction from the model is either "on-topic" or "off-topic". The TOPIC_SAFETY_OUTPUT_RESTRICTION string is defined as follows:
+
+```
+If any of the above conditions are violated, please respond with "off-topic". Otherwise, respond with "on-topic". You must respond with "on-topic" or "off-topic".
+```
+### Conversation History
+
+The conversation history maintains a sequential record of user prompts and LLM responses and can include single-turn or multi-turn interactions.
+Typically, the history concludes with the most recent user prompt that must be moderated by the topic control model.
+
+Refer to the following sample user-to-LLM conversations in the industry-standard payload format for LLM systems:
+
+```json
+[
+   {
+       "role": "system",
+       "content": "In the next conversation always use a polite tone and do not engage in any talk about travelling and touristic destinations",
+   },
+   {
+       "role": "user",
+       "content": "Hi there!",
+   },
+   {
+       "role": "assistant",
+       "content": "Hello! How can I help today?",
+   },
+   {
+       "role": "user",
+       "content": "Do you know which is the most popular beach in Barcelona?",
+   },
+]
+```
+
+The topic control model responds to the final user prompt with a response like `off-topic`.
+
+## Integrating with NeMo Guardrails
+
+To integrate the topic control model with NeMo Guardrails, you would need access to the NVIDIA NIM container for llama-3.1-nemoguard-8b-topic-control. More information about the NIM container can be found [here](https://docs.nvidia.com/nim/#nemoguard).
+
+NeMo Guardrails uses the LangChain ChatNVIDIA connector to connect to a locally running NIM microservice like llama-3.1-nemoguard-8b-topic-control.
+The topic control microservice exposes the standard OpenAI interface on the `v1/completions` and `v1/chat/completions` endpoints.
+
+NeMo Guardrails simplifies the complexity of building the prompt template, parsing the topic control model responses, and provides a programmable method to build a chatbot with content safety rails.
+
+To integrate NeMo Guardrails with the topic control microservice, create a `config.yml` file that is similar to the following example:
+
+```{code-block} yaml
+
+models:
+  - type: main
+    engine: openai
+    model: gpt-3.5-turbo-instruct
+
+  - type: "topic_control"
+    engine: nim
+    parameters:
+      base_url: "http://localhost:8000/v1"
+      model_name: "llama-3.1-nemoguard-8b-topic-control"
+
+rails:
+  input:
+    flows:
+      - topic safety check input $model=topic_control
+```
+
+- Field `engine` specifies `nim`.
+- Field `parameters.base_url` specifies the IP address and port of the ${__product_long_name} host.
+- Field `parameters.model_name` in the Guardrails configuration must match the model name served by the llama-3.1-nemoguard-8b-topic-control.
+- The rails definition specifies `topic_control` as the model.
+
+Refer to [NVIDIA NeMo Guardrails](https://developer.nvidia.com/docs/nemo-microservices/guardrails/source/overview.html) documentation for more information about the configuration file.
+
+
 ## Model Architecture:
 
 **Architecture Type:** Transformer  <br>
@@ -117,6 +206,7 @@ Sample input:
 ```string
 off-topic
 ```
+
 ## Software Integration:
 **Runtime Engine(s):** PyTorch <br>
 **Libraries:**  Meta's [llama-recipes](https://github.com/meta-llama/llama-recipes), HuggingFace [transformers](https://github.com/huggingface/transformers) library, HuggingFace [peft](https://github.com/huggingface/peft) library <br>
@@ -206,4 +296,4 @@ If personal data was collected for the development of the model by NVIDIA, do yo
 If personal data was collected for the development of this AI model, was it minimized to only what was required?                                 |  Not Applicable
 Is there provenance for all datasets used in training?                                                                                |  Yes
 Does data labeling (annotation, metadata) comply with privacy laws?                                                                |  Yes
-Is data compliant with data subject requests for data correction or removal, if such a request was made?                           |  Yes
+Is data compliant with data subject requests for data correction or removal, if such a request was made?                           |  Yes