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Model: NousResearch/Redmond-Puffin-13B
Source: Original Platform

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README.md

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+---
+language:
+- eng
+tags:
+- llama-2
+- sft
+license:
+- mit
+datasets:
+- LDJnr/Puffin
+---
+
+## **Redmond-Puffin-13b-V1.3**
+
+**The first commercially available language model released by Nous Research!**
+
+Redmond-Puffin-13B is likely the worlds first llama-2 based, fine-tuned language models, leveraging a hand curated set of 3K high quality examples, many of which take full advantage of the 4096 context length of Llama 2. This model was fine-tuned by Nous Research, with LDJ leading the training and dataset curation, along with significant dataset formation contributions by J-Supha.
+
+Special thank you to Redmond AI for sponsoring the compute.
+
+Special thank you to Emozilla for assisting with training experimentations and many issues encountered during training.
+
+Notable mentions for assisting in some of the training issues goes to: Caseus and Teknium.
+
+## Model Training
+
+Redmond-Puffin 13B-V1.3 is a new model trained for multiple epochs on a dataset of 3,000 carefully curated GPT-4 examples, most of which are long context conversations between a real human and GPT-4. 
+
+Additional data came from carefully curated sub sections of datasets such as CamelAI's Physics, Chemistry, Biology and Math.
+
+## Prompt Format
+
+The reccomended model usage is:
+
+WARNING, THE PREVIOUS RECCOMENDATION THAT SAID TO USE "### human" and "# response" WAS A CRITICAL ERROR, PLEASE USE THE ACCURATE PREFIX AND SUFFIX BELOW.
+
+```
+USER:
+
+ASSISTANT:
+```
+
+## When should I use Puffin or Hermes 2?
+
+Puffin and Hermes-2 both beat previous SOTA for GPT4ALL benchmarks, with Hermes-2 winning by a 0.1% margin over Puffin.
+
+- Hermes 2 is trained on purely single turn instruction examples.
+
+- Puffin is trained mostly on multi-turn, long context, highly curated and cleaned GPT-4 conversations with real humans, as well as curated single-turn examples relating to Physics, Bio, Math and Chem.
+
+For these reasons, it's reccomended to give Puffin a try if you want to have multi-turn conversations and/or long context communication.
+
+## Example Outputs!:
+
+![puffin](https://i.imgur.com/P0MsN8B.png)
+
+![puffin](https://i.imgur.com/8EO3ThV.png) 
+
+![puffin](https://i.imgur.com/5IWolFw.png) 
+
+![puffin](https://i.imgur.com/TQui8m7.png) 
+
+![puffin](https://i.imgur.com/tderIfl.png) 
+
+## Notable Features:
+
+ - The first Llama-2 based fine-tuned model released by Nous Research.
+
+ - Ability to recall information upto 2023 without internet (ChatGPT cut off date is in 2021)
+
+ - Pretrained on 2 trillion tokens of text. (This is double the amount of most Open LLM's)
+
+ - Pretrained with a context length of 4096 tokens, and fine-tuned on a significant amount of multi-turn conversations reaching that full token limit.
+
+ - The first commercially available language model released by Nous Research.
+
+## Current Limitations
+
+Some token mismatch problems and formatting issues have been idenitifed, these may very possibly effect the current output quality.
+
+We plan to have these solved in an updated Puffin model in the very near future, please stay tuned!
+
+## Future Plans
+
+This is a relatively early build amongst the grand plans for the future of Puffin! 
+
+Current limitations: Some token mismatch problems have been identified, these may effect the current output quality, we plan to have this solved in Puffin V2 along with other improvements.
+
+## How you can help!
+
+In the near future we plan on leveraging the help of domain specific expert volunteers to eliminate any mathematically/verifiably incorrect answers from our training curations. 
+
+If you have at-least a bachelors in mathematics, physics, biology or chemistry and would like to volunteer even just 30 minutes of your expertise time, please contact LDJ on discord!
+
+## Benchmarks!
+
+As of Puffins release, it achieves a new SOTA for the GPT4All benchmarks! Supplanting Hermes for the #1 position!
+(Rounded to nearest tenth)
+
+Previous Sota: Hermes - 68.8
+New Sota:      Puffin - 69.9 (+1.1)
+
+note: After release, Puffin has since had its average GPT4All score beaten by 0.1%, by Nous' very own Model Hermes-2!
+Latest SOTA w/ Hermes 2- 70.0 (+0.1 over Puffins 69.9 score)
+
+That being said, Puffin supplants Hermes-2 for the #1 spot in Arc-E, HellaSwag and Winogrande!
+
+Puffin also perfectly ties with Hermes in PIQA, however Hermes-2 still excels in much of Big Bench and AGIEval, so it's highly reccomended you give it a try as well!
+
+GPT4all :
+
+```
+|    Task     |Version| Metric |Value |   |Stderr|
+|-------------|------:|--------|-----:|---|-----:|
+|arc_challenge|      0|acc     |0.4983|±  |0.0146|
+|             |       |acc_norm|0.5068|±  |0.0146|
+|arc_easy     |      0|acc     |0.7980|±  |0.0082|
+|             |       |acc_norm|0.7757|±  |0.0086|
+|boolq        |      1|acc     |0.8150|±  |0.0068|
+|hellaswag    |      0|acc     |0.6132|±  |0.0049|
+|             |       |acc_norm|0.8043|±  |0.0040|
+|openbookqa   |      0|acc     |0.3560|±  |0.0214|
+|             |       |acc_norm|0.4560|±  |0.0223|
+|piqa         |      0|acc     |0.7954|±  |0.0094|
+|             |       |acc_norm|0.8069|±  |0.0092|
+|winogrande   |      0|acc     |0.7245|±  |0.0126|
+```
+
+ 
+
+```
+|                      Task                      |Version|       Metric        |Value |   |Stderr|
+|------------------------------------------------|------:|---------------------|-----:|---|-----:|
+|bigbench_causal_judgement                       |      0|multiple_choice_grade|0.5368|±  |0.0363|
+|bigbench_date_understanding                     |      0|multiple_choice_grade|0.7127|±  |0.0236|
+|bigbench_disambiguation_qa                      |      0|multiple_choice_grade|0.3023|±  |0.0286|
+|bigbench_geometric_shapes                       |      0|multiple_choice_grade|0.1003|±  |0.0159|
+|                                                |       |exact_str_match      |0.0000|±  |0.0000|
+|bigbench_logical_deduction_five_objects         |      0|multiple_choice_grade|0.2520|±  |0.0194|
+|bigbench_logical_deduction_seven_objects        |      0|multiple_choice_grade|0.1743|±  |0.0143|
+|bigbench_logical_deduction_three_objects        |      0|multiple_choice_grade|0.4200|±  |0.0285|
+|bigbench_movie_recommendation                   |      0|multiple_choice_grade|0.2900|±  |0.0203|
+|bigbench_navigate                               |      0|multiple_choice_grade|0.5000|±  |0.0158|
+|bigbench_reasoning_about_colored_objects        |      0|multiple_choice_grade|0.5430|±  |0.0111|
+|bigbench_ruin_names                             |      0|multiple_choice_grade|0.4442|±  |0.0235|
+|bigbench_salient_translation_error_detection    |      0|multiple_choice_grade|0.2074|±  |0.0128|
+|bigbench_snarks                                 |      0|multiple_choice_grade|0.5083|±  |0.0373|
+|bigbench_sports_understanding                   |      0|multiple_choice_grade|0.4970|±  |0.0159|
+|bigbench_temporal_sequences                     |      0|multiple_choice_grade|0.3260|±  |0.0148|
+|bigbench_tracking_shuffled_objects_five_objects |      0|multiple_choice_grade|0.2136|±  |0.0116|
+|bigbench_tracking_shuffled_objects_seven_objects|      0|multiple_choice_grade|0.1326|±  |0.0081|
+|bigbench_tracking_shuffled_objects_three_objects|      0|multiple_choice_grade|0.4200|±  |0.0285|
+```
+
+AGI Eval:
+
+``` 
+|             Task             |Version| Metric |Value |   |Stderr|
+|------------------------------|------:|--------|-----:|---|-----:|
+|agieval_aqua_rat              |      0|acc     |0.2283|±  |0.0264|
+|                              |       |acc_norm|0.2244|±  |0.0262|
+|agieval_logiqa_en             |      0|acc     |0.2780|±  |0.0176|
+|                              |       |acc_norm|0.3164|±  |0.0182|
+|agieval_lsat_ar               |      0|acc     |0.2348|±  |0.0280|
+|                              |       |acc_norm|0.2043|±  |0.0266|
+|agieval_lsat_lr               |      0|acc     |0.3392|±  |0.0210|
+|                              |       |acc_norm|0.2961|±  |0.0202|
+|agieval_lsat_rc               |      0|acc     |0.4387|±  |0.0303|
+|                              |       |acc_norm|0.3569|±  |0.0293|
+|agieval_sat_en                |      0|acc     |0.5874|±  |0.0344|
+|                              |       |acc_norm|0.5194|±  |0.0349|
+|agieval_sat_en_without_passage|      0|acc     |0.4223|±  |0.0345|
+|                              |       |acc_norm|0.3447|±  |0.0332|
+|agieval_sat_math              |      0|acc     |0.3364|±  |0.0319|
+|                              |       |acc_norm|0.2773|±  |0.0302|
+```

config.json

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+{
+  "_name_or_path": "output/puffin-v1.3-4k-sharegpt-llama-2-13b/checkpoint-550",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 4096,
+  "model_type": "llama",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 40,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.32.0.dev0",
+  "use_cache": false,
+  "vocab_size": 32032
+}

configuration.json

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+  "top_p": 0.6,
+  "transformers_version": "4.32.0.dev0"
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special_tokens_map.json

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tokenizer_config.json

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