justtherightsize
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zephyr-7b-sft-full124_d270

Text Generation

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conversational

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justtherightsize commited on Sep 20

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 ---
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
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-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+license: mit
+datasets:
+  - facebook/empathetic_dialogues
+language:
+  - en
+base_model: alignment-handbook/zephyr-7b-sft-full
+widget:
+  - example_title: Pirate!
+    messages:
+      - role: system
+        content: You are a friendly assistant, who provides empathetic responses to the user. The input contains previous turn of the dialog, where each utterance is prefaced with tags <|user>, or <|assistant|>. Be empathetic and precise. Make sure to give responses that make the dialogue flow. Avoid repeating the prompt. Please respond creatively and expressively to make the responses longer. You can offer advice.
+      - role: user
+        content: Yeah about 10 years ago I had a horrifying experience. It was 100% their fault but they hit the water barrels and survived. They had no injuries but they almost ran me off the road.
+      - role: assistant
+        content: Did you suffer any injuries?
+      - role: user
+        content: No I wasn't hit. It turned out they were drunk. I felt guilty but realized it was his fault.
+    output:
+      text: >-
+        That's good that you didn't get hurt. I hope they got in trouble for driving drunk.
+pipeline_tag: text-generation
+model-index:
+  - name: justtherightsize/zephyr-7b-sft-full124_d270
+    results:
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: Open LLM Leaderboard
+          type: various
+          config: various
+          split: various
+          args:
+            num_few_shot: 5
+        metrics:
+          - type: acc
+            name: accuracy
+            value: 0.2665
+        source:
+          name: Open LLM Leaderboard
+          url: >-
+            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard
+      - task:
+          type: text-generation
+          name: Text Generation
+        dataset:
+          name: MMLU (5-Shot)
+          type: cais/mmlu
+          config: all
+          split: test
+          args:
+            num_few_shot: 5
+        metrics:
+          - type: acc
+            name: accuracy
+            value: 58.38
+        source:
+          name: MMLU
+          url: >-
+            https://github.com/huggingface/lm-evaluation-harness.git
+---
+# Model Card for zephyr-7b-sft-full124_d270
+This model paricipated in multi-turn dialogues and responses empathetically.
+## Model Description
+ We propose a data-driven solution for Empathetic Response Generation with LLMs: aligning LLMs via preference optimization algorithms. First, we build a preference dataset using the benchmark dataset EmpatheticDialogues (Rashkin et al., 2019). It contains short multi-turn human-to-human dialogues grounded by emotion labels. We leverage this emotion grounding to sample dialog completions labeled with polar opposite emotions using Plutchik’s wheel (Plutchik, 2001) such that each prompt is paired with preferred and non-preferred completions. We then fine-tune a foundational LLM using Direct Preference Optimization (DPO) (Rafailov et al., 2024) to generate responses aligned with the preferred candidate response.
+- **Developed by:** TBA
+- **Model type:** Autoregressive Encoder-Decoder
+- **Language(s):** en
+- **Finetuned from:** alignment-handbook/zephyr-7b-sft-full
+## Sources
+- **Repository:** <https://github.com/justtherightsize/empo>
+- **(*non-anonymized*) Paper preprint:** <https://arxiv.org/abs/2406.19071>
+## Usage
+TODO
+## Out-of-Scope Usage
+Note that fine-tuning on the EmpatheticDialogues caused some specialization.
+## Training
+TODO
+## Cite
+TBA, now please cite the **non-anonymized** [preprint](https://arxiv.org/abs/2305.15017)