Upload README.md

README.md

@@ -1,3 +1,106 @@
----
-license: gemma
----
+---
+base_model: google/gemma-2-2b-jpn-it
+language:
+- multilingual
+datasets:
+  - mlabonne/orpo-dpo-mix-40k
+library_name: transformers
+license: gemma
+license_link: https://ai.google.dev/gemma/terms
+pipeline_tag: text-generation
+tags:
+- nlp
+- code
+quantized_by: ymcki
+widget:
+- messages:
+  - role: user
+    content: Can you provide ways to eat combinations of bananas and dragonfruits?
+---
+
+Original model: https://huggingface.co/google/gemma-2-2b-jpn-it
+
+## Prompt format
+
+```
+<start_of_turn>user
+{prompt}<end_of_turn>
+<start_of_turn>model
+<end_of_turn>
+<start_of_turn>model
+
+```
+
+Note that this model does not support a System prompt.
+
+This is abliterated model of [google/gemma-2-2b-jpn-it](https://huggingface.co/google/gemma-2-2b-jpn-it) using the 
+[method](https://medium.com/@mlabonne/uncensor-any-llm-with-abliteration-d30148b7d43e) 
+described by mlabonne.
+
+Layer 17 of the original model was chosen for abliteration.
+I also created another layer 18 abliterated model for comparison.
+
+ORPO fine tuning was performed for four epoches.
+
+| Epoch | loss | eval_loss |
+| ----- | ---- | --------- |
+| 1 | 10.51274610161781342 | 11.023366928100586 |
+| 2 | 10.09700682163238566 | 10.434176445007324 |
+| 3 | 10.35771694183349566 | 10.179500579833984 |
+| 4 | 10.82988178133964582 | 10.084120750427246 |
+
+The fine tuned model is uploaded here to be evaluated by the Open LLM Leaderboard to see if the brain damaged 
+suffered by the non-ORPO model can be healed.
+
+## Benchmark (100.0*raw scores only)
+
+Click on the model name go to the raw score json generated by Open LLM Leaderboard.
+
+| Model | Average | IFEval | BHH | Math Lv5 | GPQA | MUSR | MMLU-PRO |
+| ----- | ------- | ------ | ----|--------- | ---- | ---- | -------- |
+| [gemma-2-2b-jpn-it](https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/google/gemma-2-2b-jpn-it/results_2024-10-15T15-21-39.173019.json) | 30.82 | 54.11 | 41.43 | 0.0 | 27.52 | 37.17 | 24.67 |
+| gemma-2-2b-jpn-it-abliterated-17-ORPO | TBD | TBD | TBD | TBD | TBD | TBD | TBD |
+| [gemma-2-2b-jpn-it-abliterated-17](https://huggingface.co/datasets/open-llm-leaderboard/results/raw/main/ymcki/gemma-2-2b-jpn-it-abliterated-17/results_2024-10-17T11-26-10.721815.json) | 16.74 | 0.0 | 29.13 | 0.0 | 25.92 | 33.73 | 11.68 |
+| [gemma-2-2b-jpn-it-abliterated-18](https://huggingface.co/datasets/open-llm-leaderboard/results/raw/main/ymcki/gemma-2-2b-jpn-it-abliterated-18/results_2024-10-16T07-58-03.781979.json) | 16.74 | 0.0 | 29.13 | 0.0 | 25.92 | 33.73 | 11.68 |
+
+Indeed, it is quite dumbed down relative to the original. Interestingly, both abliteration models have the same Open LLM results. 
+
+## How to run this model
+
+```py
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import transformers
+import torch
+
+model_id = "gemma-2-2b-jpn-it-abliterated-17-ORPO"
+dtype = torch.bfloat16
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="cuda",
+    torch_dtype=dtype,)
+
+chat = [
+    { "role": "user", "content": "Write a hello world program" },
+]
+prompt = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+```
+
+## Downloading using huggingface-cli
+
+First, make sure you have hugginface-cli installed:
+
+```
+pip install -U "huggingface_hub[cli]"
+```
+
+Then, you can target the specific file you want:
+
+```
+huggingface-cli download ymcki/gemma-2-2b-jpn-it-abliterated-17-ORPO --include "*" --local-dir ./
+```
+
+## Credits
+
+Thank you mlabonne for describing his abliteration method.