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Model: rasa/cmd_gen_llama_3.1_8b_calm_demo Source: Original Platform
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README.md
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README.md
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---
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base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
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language:
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- en
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license: apache-2.0
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tags:
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- text-generation-inference
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- transformers
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- 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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### Model Description
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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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* `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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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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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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- **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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## Uses
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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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### 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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### 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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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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## 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).
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config.json
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"use_cache": true,
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"vocab_size": 128256
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}
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"model.layers.7.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|finetune_right_pad_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
410563
tokenizer.json
Normal file
410563
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2064
tokenizer_config.json
Normal file
2064
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user