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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+juanako-7b-UNA - GGUF
+- Model creator: https://huggingface.co/fblgit/
+- Original model: https://huggingface.co/fblgit/juanako-7b-UNA/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [juanako-7b-UNA.Q2_K.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q2_K.gguf) | Q2_K | 2.53GB |
+| [juanako-7b-UNA.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.IQ3_XS.gguf) | IQ3_XS | 2.81GB |
+| [juanako-7b-UNA.IQ3_S.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.IQ3_S.gguf) | IQ3_S | 2.96GB |
+| [juanako-7b-UNA.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q3_K_S.gguf) | Q3_K_S | 2.95GB |
+| [juanako-7b-UNA.IQ3_M.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.IQ3_M.gguf) | IQ3_M | 3.06GB |
+| [juanako-7b-UNA.Q3_K.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q3_K.gguf) | Q3_K | 3.28GB |
+| [juanako-7b-UNA.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q3_K_M.gguf) | Q3_K_M | 3.28GB |
+| [juanako-7b-UNA.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q3_K_L.gguf) | Q3_K_L | 3.56GB |
+| [juanako-7b-UNA.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.IQ4_XS.gguf) | IQ4_XS | 3.67GB |
+| [juanako-7b-UNA.Q4_0.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q4_0.gguf) | Q4_0 | 3.83GB |
+| [juanako-7b-UNA.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.IQ4_NL.gguf) | IQ4_NL | 0.92GB |
+| [juanako-7b-UNA.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q4_K_S.gguf) | Q4_K_S | 0.07GB |
+| [juanako-7b-UNA.Q4_K.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q4_K.gguf) | Q4_K | 0.0GB |
+| [juanako-7b-UNA.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q4_K_M.gguf) | Q4_K_M | 0.0GB |
+| [juanako-7b-UNA.Q4_1.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q4_1.gguf) | Q4_1 | 0.0GB |
+| [juanako-7b-UNA.Q5_0.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q5_0.gguf) | Q5_0 | 0.0GB |
+| [juanako-7b-UNA.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q5_K_S.gguf) | Q5_K_S | 0.0GB |
+| [juanako-7b-UNA.Q5_K.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q5_K.gguf) | Q5_K | 0.0GB |
+| [juanako-7b-UNA.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q5_K_M.gguf) | Q5_K_M | 0.0GB |
+| [juanako-7b-UNA.Q5_1.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q5_1.gguf) | Q5_1 | 0.0GB |
+| [juanako-7b-UNA.Q6_K.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q6_K.gguf) | Q6_K | 0.0GB |
+| [juanako-7b-UNA.Q8_0.gguf](https://huggingface.co/RichardErkhov/fblgit_-_juanako-7b-UNA-gguf/blob/main/juanako-7b-UNA.Q8_0.gguf) | Q8_0 | 0.0GB |
+
+
+
+
+Original model description:
+---
+license: apache-2.0
+tags:
+- alignment-handbook
+- generated_from_trainer
+- juanako
+- mistral
+- UNA
+datasets:
+- HuggingFaceH4/ultrafeedback_binarized
+model-index:
+- name: juanako-7b-UNA
+  results:
+  - task:
+      type: text-generation
+      name: TruthfulQA (MC2)
+    dataset:
+      name: truthful_qa
+      type: text-generation
+      config: multiple_choice
+      split: validation
+    metrics:
+    - type: accuracy
+      value: 65.13
+      verified: true
+  - task:
+      type: text-generation
+      name: ARC-Challenge
+    dataset:
+      name: ai2_arc
+      type: text-generation
+      config: ARC-Challenge
+      split: test
+    metrics:
+    - type: accuracy
+      value: 68.17
+      verified: true
+  - task:
+      type: text-generation
+      name: HellaSwag
+    dataset:
+      name: Rowan/hellaswag
+      type: text-generation
+      split: test
+    metrics:
+    - type: accuracy
+      value: 85.34
+      verified: true
+    - type: accuracy
+      value: 83.57
+  - task:
+      type: text-generation
+      name: Winogrande
+    dataset:
+      name: winogrande
+      type: text-generation
+      config: winogrande_debiased
+      split: test
+    metrics:
+    - type: accuracy
+      value: 78.85
+      verified: true
+  - task:
+      type: text-generation
+      name: MMLU
+    dataset:
+      name: cais/mmlu
+      type: text-generation
+      config: all
+      split: test
+    metrics:
+    - type: accuracy
+      value: 62.47
+      verified: true
+  - task:
+      type: text-generation
+      name: DROP
+    dataset:
+      name: drop
+      type: text-generation
+      split: validation
+    metrics:
+    - type: accuracy
+      value: 38.74
+      verified: true
+  - task:
+      type: text-generation
+      name: PubMedQA
+    dataset:
+      name: bigbio/pubmed_qa
+      type: text-generation
+      config: pubmed_qa_artificial_bigbio_qa
+      split: validation
+    metrics:
+    - type: accuracy
+      value: 76.0
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: AI2 Reasoning Challenge (25-Shot)
+      type: ai2_arc
+      config: ARC-Challenge
+      split: test
+      args:
+        num_few_shot: 25
+    metrics:
+    - type: acc_norm
+      value: 68.17
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: HellaSwag (10-Shot)
+      type: hellaswag
+      split: validation
+      args:
+        num_few_shot: 10
+    metrics:
+    - type: acc_norm
+      value: 85.34
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
+      name: 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
+      value: 62.47
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: TruthfulQA (0-shot)
+      type: truthful_qa
+      config: multiple_choice
+      split: validation
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: mc2
+      value: 65.13
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: Winogrande (5-shot)
+      type: winogrande
+      config: winogrande_xl
+      split: validation
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 78.85
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GSM8k (5-shot)
+      type: gsm8k
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 44.81
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/juanako-7b-UNA
+      name: Open LLM Leaderboard
+---
+
+# juanako-7b-UNA (Uniform Neural Alignment)
+
+This model is a fine-tuned version of [fblgit/juanako-7b-UNA-v2-phase-1](https://huggingface.co/fblgit/juanako-7b-UNA-v2-phase-1) on the HuggingFaceH4/ultrafeedback_binarized dataset.
+It outperforms in many aspects most of the current Mistral based models and is the **latest and most powerful juanako version as of now**.
+
+## Scores
+
+The official HuggingFace results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/results/blob/main/fblgit/juanako-7b-UNA/results_2023-11-28T08-33-33.965228.json)
+
+| Model | Average ⬆️| ARC (25-s) ⬆️ | HellaSwag (10-s) ⬆️ | MMLU (5-s) ⬆️| TruthfulQA (MC) (0-s) ⬆️ | Winogrande (5-s) | GSM8K (5-s) | DROP (3-s) |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- |
+|[mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) | 50.32 | 59.58  | 83.31  | 64.16  | 42.15 | 78.37 | 18.12 | 6.14 |
+| [Intel/neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1) | 59.0 | 66.21 | 83.64 | 62.37  | 59.65 | 78.14 | 19.56 | 43.84 |
+| [fblgit/juanako-7b-UNA](https://huggingface.co/fblgit/juanako-7b-UNA) | **59.91** | **68.17** | **85.34** | 62.47  | **65.13** | **78.85** | **20.7** | 38.74 |
+
+It scores: **59.91** according HuggingFace LLM Leaderboard.
+It scores: **65.1** with `big-refactor` branch of lm-eval-harness
+
+Author [Xavier M.](mailto:[email protected]) @fblgit
+
+## Model description
+
+juanako uses UNA, Uniform Neural Alignment. A training technique that ease alignment between transformer layers yet to be published.
+
+### Prompts
+The following prompts showed positive results, it may depend the task and needs further experimentation but this should work for starters:
+```
+<|im_start|>system
+- You are a helpful assistant chatbot trained by MosaicML.
+- You answer questions.
+- You are excited to be able to help the user, but will refuse to do anything that could be considered harmful to the user.
+- You are more than just an information source, you are also able to write poetry, short stories, and make jokes.<|im_end|>
+<|im_start|>user
+Explain QKV<|im_end|>
+<|im_start|>assistant
+```
+```
+### Assistant: I am StableVicuna, a large language model created by CarperAI. I am here to chat!
+
+### Human: Explain QKV
+### Assistant:
+```
+```
+[Round <|round|>]
+问：Explain QKV
+答：
+```
+```
+[Round <|round|>]
+Question：Explain QKV
+Answer：
+```
+```
+Question：Explain QKV
+Answer：
+```
+
+## Evaluations (lm-eval big-refactor branch)
+
+### TruthfulQA 0-Shot
+```
+|    Tasks     |Version|Filter|Metric|Value |   |Stderr|
+|--------------|-------|------|------|-----:|---|-----:|
+|truthfulqa_mc2|Yaml   |none  |acc   |0.6549|±  |0.0153|
+```
+### ARC 25-Shot
+```
+|    Tasks    |Version|Filter| Metric |Value |   |Stderr|
+|-------------|-------|------|--------|-----:|---|-----:|
+|arc_challenge|Yaml   |none  |acc     |0.6476|±  |0.0140|
+|             |       |none  |acc_norm|0.6809|±  |0.0136|
+```
+### HellaSwag 10-Shot
+```
+|  Tasks  |Version|Filter| Metric |Value |   |Stderr|
+|---------|-------|------|--------|-----:|---|-----:|
+|hellaswag|Yaml   |none  |acc     |0.6703|±  |0.0047|
+|         |       |none  |acc_norm|0.8520|±  |0.0035|
+```
+### GSM8k 5-Shot
+```
+|Tasks|Version|  Filter  |  Metric   |Value |   |Stderr|
+|-----|-------|----------|-----------|-----:|---|-----:|
+|gsm8k|Yaml   |get-answer|exact_match|0.4898|±  |0.0138|
+```
+### GPT Evaluations 0-Shot
+```
+|    Tasks     |Version|Filter|  Metric  |Value |   |Stderr|
+|--------------|-------|------|----------|-----:|---|-----:|
+|boolq         |Yaml   |none  |acc       |0.8703|±  |0.0059|
+|lambada_openai|Yaml   |none  |perplexity|3.2598|±  |0.0705|
+|              |       |none  |acc       |0.7336|±  |0.0062|
+|piqa          |Yaml   |none  |acc       |0.8254|±  |0.0089|
+|              |       |none  |acc_norm  |0.8292|±  |0.0088|
+|sciq          |Yaml   |none  |acc       |0.9580|±  |0.0063|
+|              |       |none  |acc_norm  |0.9130|±  |0.0089|
+```
+### MathQA 0-Shot
+```
+|Tasks |Version|Filter| Metric |Value |   |Stderr|
+|------|-------|------|--------|-----:|---|-----:|
+|mathqa|Yaml   |none  |acc     |0.3752|±  |0.0089|
+|      |       |none  |acc_norm|0.3772|±  |0.0089|
+```
+### PiQa 1-Shot
+```
+|Tasks|Version|Filter| Metric |Value |   |Stderr|
+|-----|-------|------|--------|-----:|---|-----:|
+|piqa |Yaml   |none  |acc     |0.8308|±  |0.0087|
+|     |       |none  |acc_norm|0.8357|±  |0.0086|
+```
+### Winogrande 5-Shot
+```
+|  Tasks   |Version|Filter|Metric|Value|   |Stderr|
+|----------|-------|------|------|----:|---|-----:|
+|winogrande|Yaml   |none  |acc   |0.768|±  |0.0119|
+```
+### PubMedQA 0-Shot
+```
+| Tasks  |Version|Filter|Metric|Value|   |Stderr|
+|--------|-------|------|------|----:|---|-----:|
+|pubmedqa|Yaml   |none  |acc   | 0.76|±  |0.0191|
+```
+### RACE 1-Shot
+```
+|Tasks|Version|Filter|Metric|Value |   |Stderr|
+|-----|-------|------|------|-----:|---|-----:|
+|race |Yaml   |none  |acc   |0.5282|±  |0.0154|
+```
+### MMLU 5-Shot (8-Bit)
+```
+|      Groups      |Version|Filter|Metric|Value |   |Stderr|
+|------------------|-------|------|------|-----:|---|-----:|
+|mmlu              |N/A    |none  |acc   |0.6137|±  |0.1243|
+| - humanities     |N/A    |none  |acc   |0.5671|±  |0.1101|
+| - other          |N/A    |none  |acc   |0.6859|±  |0.1164|
+| - social_sciences|N/A    |none  |acc   |0.7195|±  |0.0713|
+| - stem           |N/A    |none  |acc   |0.5087|±  |0.1297|
+```
+### DROP 3-Shot (8-Bit) (Instruct-Eval)
+```
+{'score': 0.49801113762927607}
+{'drop': 49.8}
+drop: 49.8
+```
+
+### CRASS 0-Shot (Instruct-Eval)
+```
+{'score': 0.8357664233576643}
+{'crass': 83.58}
+crass: 83.58
+```
+
+## Training Details
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0001
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 14
+- gradient_accumulation_steps: 16
+- total_train_batch_size: 224
+- total_eval_batch_size: 14
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.01
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
+|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
+| 0.4795        | 0.2   | 56   | 0.4958          | -1.3684        | -2.6385          | 0.7552             | 1.2701          | -265.3887      | -241.2612    | -2.2572         | -2.4922       |
+| 0.4642        | 0.4   | 112  | 0.4859          | -1.0380        | -1.9769          | 0.7273             | 0.9389          | -258.7718      | -237.9569    | -2.2414         | -2.4751       |
+| 0.4758        | 0.61  | 168  | 0.4808          | -1.2594        | -2.3704          | 0.7343             | 1.1110          | -262.7074      | -240.1708    | -2.2305         | -2.4633       |
+| 0.4549        | 0.81  | 224  | 0.4768          | -1.1906        | -2.3201          | 0.7552             | 1.1295          | -262.2044      | -239.4827    | -2.2284         | -2.4610       |
+
+
+### Framework versions
+
+- Transformers 4.35.0-UNA
+- Pytorch 2.1.0
+- Datasets 2.14.6
+- Tokenizers 0.14.1
+
+## Citations
+If you find juanako useful please:
+
+```
+@misc{juanako7buna,
+  title={Juanako: Uniform Neural Alignment}, 
+  author={Xavier Murias},
+  year={2023},
+  publisher = {HuggingFace},
+  journal = {HuggingFace repository},
+  howpublished = {\url{https://huggingface.co/fblgit/juanako-7b-UNA}},
+}
+```
+
+Thanks to all the brilliant humans behind the creation of AI, here some of the ones that we find relevant to our research. If you feel a citation is missing, please contact.
+```
+@misc{lin2021truthfulqa,
+  title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
+  author={Stephanie Lin and Jacob Hilton and Owain Evans},
+  year={2021},
+  eprint={2109.07958},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL}
+}
+@misc{tunstall2023zephyr,
+      title={Zephyr: Direct Distillation of LM Alignment}, 
+      author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf},
+      year={2023},
+      eprint={2310.16944},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+@inproceedings{Bisk2020,
+  author = {Yonatan Bisk and Rowan Zellers and
+            Ronan Le Bras and Jianfeng Gao
+            and Yejin Choi},
+  title = {PIQA: Reasoning about Physical Commonsense in
+           Natural Language},
+  booktitle = {Thirty-Fourth AAAI Conference on
+               Artificial Intelligence},
+  year = {2020},
+}
+@software{eval-harness,
+  author       = {Gao, Leo and
+                  Tow, Jonathan and
+                  Biderman, Stella and
+                  Black, Sid and
+                  DiPofi, Anthony and
+                  Foster, Charles and
+                  Golding, Laurence and
+                  Hsu, Jeffrey and
+                  McDonell, Kyle and
+                  Muennighoff, Niklas and
+                  Phang, Jason and
+                  Reynolds, Laria and
+                  Tang, Eric and
+                  Thite, Anish and
+                  Wang, Ben and
+                  Wang, Kevin and
+                  Zou, Andy},
+  title        = {A framework for few-shot language model evaluation},
+  month        = sep,
+  year         = 2021,
+  publisher    = {Zenodo},
+  version      = {v0.0.1},
+  doi          = {10.5281/zenodo.5371628},
+  url          = {https://doi.org/10.5281/zenodo.5371628}
+}
+@misc{rafailov2023direct,
+    title={Direct Preference Optimization: Your Language Model is Secretly a Reward Model}, 
+    author={Rafael Rafailov and Archit Sharma and Eric Mitchell and Stefano Ermon and Christopher D. Manning and Chelsea Finn},
+    year={2023},
+    eprint={2305.18290},
+    archivePrefix={arXiv},
+}
+```
+
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__juanako-7b-UNA)
+
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |67.46|
+|AI2 Reasoning Challenge (25-Shot)|68.17|
+|HellaSwag (10-Shot)              |85.34|
+|MMLU (5-Shot)                    |62.47|
+|TruthfulQA (0-shot)              |65.13|
+|Winogrande (5-shot)              |78.85|
+|GSM8k (5-shot)                   |44.81|
+
+
+