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Model: rasa/command-generator-llama-3.1-8b-instruct
Source: Original Platform

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README.md

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+---
+base_model: meta-llama/Llama-3.1-8B-Instruct
+language:
+- en
+license: apache-2.0
+tags:
+- text-generation-inference
+- transformers
+- llama
+- trl
+- sft
+datasets:
+- rasa/command-generation-calm-v2
+pipeline_tag: text-generation
+---
+
+# Model Card for Command Generator
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+This is a Dialogue Understanding (DU) model developed by Rasa. 
+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.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+This model takes as input a transcript of an ongoing conversation between an AI assistant and a user,
+as well as structured information about the assistant's business logic.
+As output, it produces a short sequence of [commands](https://rasa.com/docs/rasa-pro/concepts/dialogue-understanding#command-reference) 
+(typically 1-3) from the following list:
+
+* `start flow flow_name`
+* `set slot slot_name slot_value`
+* `cancel flow`
+* `disambiguate flows flow_name1 flow_name2 ... flow_name_n`
+* `provide info`
+* `offtopic reply`
+* `hand over`
+
+Note that this model can only produce commands to be interpreted by Rasa.
+It **cannot** be used to generate arbitrary text.
+
+The Command Generator translates user messages into this internal grammar, allowing CALM to progress the conversation.
+
+Examples:
+
+> I want to transfer money
+
+`start flow transfer_money`
+
+> I want to transfer $55 to John
+
+```
+start flow transfer_money
+set slot recipient John
+set slot amount 55
+```
+
+- **Developed by:** Rasa Technologies
+- **Model type:** Text Generation
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Finetuned from model [optional]:** [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
+
+
+## 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 Command Generator is used as part of an AI assistant developed with Rasa's CALM paradigm. 
+Typical use cases include customer-facing chatbots, voice assistants, IVR systems, and internal chatbots in large organizations.
+
+### Direct Use
+
+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).
+
+### Downstream Use [optional]
+
+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
+
+### Out-of-Scope Use
+
+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.
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+The Command Generator interprets conversations and translates user messages into commands.
+These commands are processed by Rasa to advance the conversations. 
+This model does not generate text to be sent to and end user, and is incapable of generating problematic
+or harmful text. 
+
+However, as with any pre-trained model, its predictions are susceptible to bias.
+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.
+
+
+## Training Details
+
+### Training Data
+
+<!-- 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. -->
+
+Trained on the `train` split of [rasa/command-generation-calm-v2](https://huggingface.co/datasets/rasa/command-generation-calm-v2).
+
+### Training Procedure 
+
+Trained using the notebook available [here](https://github.com/RasaHQ/notebooks/blob/main/cmd_gen_finetuning.ipynb). Used a single A100 GPU with 40GB VRAM.
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+Evaluated on the `test` split of [rasa/command-generation-calm-v2](https://huggingface.co/datasets/rasa/command-generation-calm-v2).
+
+
+#### Metrics
+
+F1 score per command type (StartFlow, SetSlot, etc.) is the main metric chosen to evaluate the model on the test split. 
+This helps us understand which commands the model has learnt well and the ones that the model needs more training on.
+
+### Results
+
+To be added
+
+## Model Card Contact
+
+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).

config.json

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+  "architectures": [
+    "LlamaForCausalLM"
+  ],
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+  "initializer_range": 0.02,
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+  "max_position_embeddings": 131072,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": {
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+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 8192,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.49.0",
+  "use_cache": false,
+  "vocab_size": 128256
+}

generation_config.json

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+{
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+  "do_sample": true,
+  "eos_token_id": [
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+    128008,
+    128009
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+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.49.0"
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