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  - llama
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  - trl
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  - sft
 
 
 
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  ---
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- # Uploaded model
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- - **Developed by:** dakshvar22
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- - **License:** apache-2.0
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- - **Finetuned from model :** unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
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- This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
 
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- [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  - llama
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  - trl
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  - sft
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+ datasets:
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+ - rasa/command-generation-calm-demo-v1
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+ pipeline_tag: text-generation
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  ---
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+ # Model Card for Command Generator
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+ <!-- Provide a quick summary of what the model is/does. -->
 
 
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+ This is a Dialogue Understanding (DU) model developed by Rasa.
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+ It can be used to power assistants built with the [Conversational AI with Language Models (CALM) approach](https://rasa.com/docs/rasa-pro/calm) developed by Rasa.
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ <!-- Provide a longer summary of what this model is. -->
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+ This model takes as input a transcript of an ongoing conversation between an AI assistant and a user,
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+ as well as structured information about the assistant's business logic.
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+ As output, it produces a short sequence of [commands](https://rasa.com/docs/rasa-pro/concepts/dialogue-understanding#command-reference)
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+ (typically 1-3) from the following list:
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+
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+ * `StartFlow(flow_name)`
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+ * `SetSlot(slot_name, slot_value)`
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+ * `CorrectSlot(slot_name, slot_value)`
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+ * `Clarify(flow_name_1, flow_name_2, ...)`
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+ * `ChitChat`
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+ * `KnowledgeAnswer`
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+ * `HumanHandoff`
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+ * `Error`
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+
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+ Note that this model can only produce commands to be interpreted by Rasa.
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+ It **cannot** be used to generate arbitrary text.
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+
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+ The Command Generator translates user messages into this internal grammar, allowing CALM to progress the conversation.
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+ Examples:
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+ > I want to transfer money
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+ `StartFlow(transfer_money)`
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+ > I want to transfer $55 to John
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+ `StartFlow(transfer_money), SetSlot(recipient, John), SetSlot(amount, 55)`
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+
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+ - **Developed by:** Rasa Technologies
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+ - **Model type:** Text Generation
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+ - **Language(s) (NLP):** English
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+ - **License:** Apache 2.0
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+ - **Finetuned from model [optional]:** [unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit)
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+
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+
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+ ## Uses
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+
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+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+ The Command Generator is used as part of an AI assistant developed with Rasa's CALM paradigm.
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+ Typical use cases include customer-facing chatbots, voice assistants, IVR systems, and internal chatbots in large organizations.
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+
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+ ### Direct Use
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+ This model can be directly used as part of the command generator component if the flows of your CALM assistant are similar to flows used in the [rasa-calm-demo assistant](https://github.com/RasaHQ/rasa-calm-demo).
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+
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+ ### Downstream Use [optional]
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+ The model can also be used as a base model to fine-tune further on your own assistant's data using the [fine-tuning recipe feature](https://rasa.com/docs/rasa-pro/building-assistants/fine-tuning-recipe#step-2-prepare-the-fine-tuning-dataset) available in rasa pro
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+ ### Out-of-Scope Use
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+ Since the model has been explicitly fine-tuned to output the grammar of commands, it shouldn't be used to generate any other free form content.
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+ ## Bias, Risks, and Limitations
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+ The Command Generator interprets conversations and translates user messages into commands.
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+ These commands are processed by Rasa to advance the conversations.
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+ This model does not generate text to be sent to and end user, and is incapable of generating problematic
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+ or harmful text.
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+
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+ However, as with any pre-trained model, its predictions are susceptible to bias.
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+ For example, the accuracy of the model varies with the language used. The authors have tested the performance on English but haven't tried the model in any other language.
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+ ## Training Details
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+ ### Training Data
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+ <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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+ Trained on the `train` split of [rasa/command-generation-calm-demo-v1](https://huggingface.co/datasets/rasa/command-generation-calm-demo-v1).
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+ ### Training Procedure
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+ Trained using the notebook available [here](https://github.com/RasaHQ/notebooks/blob/main/cmd_gen_finetuning.ipynb). Used a single A100 GPU with 80GB VRAM.
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+
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+ ## Evaluation
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+ ### Testing Data, Factors & Metrics
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+ #### Testing Data
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+ Evaluated on the `test` split of [rasa/command-generation-calm-demo-v1](https://huggingface.co/datasets/rasa/command-generation-calm-demo-v1).
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+ #### Metrics
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+ F1 score per command type (StartFlow, SetSlot, etc.) is the main metric chosen to evaluate the model on the test split.
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+ This helps us understand which commands the model has learnt well and the ones that the model needs more training on.
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+ ### Results
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+ To be added
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+ ## Model Card Contact
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+ If you have questions about the dataset, please reach out to us on the [Rasa forum](https://forum.rasa.com/c/rasa-pro-calm/36).