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LICENSE

@@ -0,0 +1,324 @@
+                 Yi Series Models Community License Agreement
+                                 Version: 2.1
+                       Date of Release: November 23, 2023
+ 
+1. Definition
+
+“Agreement” refers to the terms and conditions defined in this Yi Series Models 
+Community License Agreement for the use, reproduction and distribution of Yi 
+Series Models.
+ 
+“Model” refers to associated components (including checkpoints) developed based
+on machine learning, including learned weights and parameters (including the
+status of optimizer).
+ 
+“Yi Series Models” refers to opensource models with different specifications and
+capabilities named “Yi” provided by the Licensor, including Yi-6B, Yi-34B etc.
+ 
+“Derivatives” refers to all modifications to Yi Series Models, work based on Yi
+Series Models, or any other models created or initialized by transferring the
+weights, parameters, activations, or output patterns of Yi Series Models to
+other models to achieve similar performance, including but not limited to
+methods that require using intermediate data representations or generating
+synthetic data based on Yi Series Models to train other models.
+ 
+“Licensor” refers to Beijing Lingyiwanwu Information Technology Co., Ltd.
+ 
+“you” refers to an individual or legal entity that exercises the license granted
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+“Third Party” refers to any individuals, legal entities or non-legal 
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+“Commercial Purposes” refers to the use of the Yi Series Models, directly or
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+2. License and License Restrictions
+The Licensor hereby grants you a non-exclusive, global, non-transferable,
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+1) Your use of the Yi Series Models must comply with the Laws and Regulations as
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+ 
+If you plan to use the Yi Series Models and Derivatives for Commercial Purposes, 
+please refer to the Registration Form of Yi Series Models for Commercial Purposes 
+(“Registration Form”), as provided in Attachment 1 of the Yi Series Models 
+Commercial License Agreement (available at https://www.lingyiwanwu.com/yi-license) 
+and send completed Registration Form to the email: yi@01.ai to complete the 
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+and restrictions specified under the Yi Series Models Commercial License Agreement.
+ 
+ 
+3. Intellectual Property
+The ownership of the Yi Series Models and their related intellectual property
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+ 
+In any circumstance, without the prior written consent of the Licensor, you are
+not allowed to use any Logo associated with the Licensor. If your use of
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+4. Disclaimer and Limitation of Liability
+
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+1) The Licensor is not responsible for data security risks resulting from your
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+2) The Yi Series Models may contain Personal Information. When you use Yi Series
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+4) The Licensor is not liable for intellectual property risks associated with
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+If your use, reproduction, distribution of the Yi Series Models, or the creation
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+ 
+5. Dispute Resolution
+
+The stipulation, effectiveness, interpretation, performance, modification, and
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+ 
+Any disputes arising from the use, copy or distribution of the Yi Series Models
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+ 
+6. Effectiveness and Termination of the Agreement
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+Your use of the Yi Series Models signifies that you have read and agreed to be
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+ 
+Upon termination of the Agreement, you must immediately cease using the Yi
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+ 
+ 
+7. Updates to the Agreement and Contact Information
+
+The Licensor reserves the right to update the Agreement from time to time. The
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+ 
+For any questions related to licensing and copyright, please contact the
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+
+
+
+                     Yi系列模型社区许可协议
+                          版本: 2.1
+                    发布日期: 2023年11月23日
+ 
+1. 定义
+
+“协议”是指本协议中定义Yi系列模型使用、复制和分发的条款和条件。
+
+“模型”是指任何附带的基于机器学习的组件(包括检查点)，包括学习的权重、参数（包括优
+化器状态）。
+
+“Yi系列模型”是指许可方开源的以Yi命名的不同规格、不同能力的模型，包括
+Yi-6B、Yi-34B等。
+
+“模型衍生品”是指对Yi系列模型的所有修改、基于Yi系列模型的工作，或通过将Yi系列模型
+的权重、参数、激活或输出模式转移到其他模型而创建或初始化的任何其他模型，以使其他
+模型的性能与Yi系列模型类似，包括但不限于需要使用中间数据表示的提取方法或基于Yi系
+列模型生成合成数据来训练其他模型的方法。
+
+“许可方”是指北京零一万物信息技术有限公司。
+
+“您”是指行使本协议授予的权限和/或出于任何目的和在任何使用领域使用Yi系列模型的个
+人或法人实体。
+ 
+“第三方”是指您之外的任何个人、法人实体或非法人组织。
+ 
+“分发”是指向第三方传输、复制、发布或以其他方式共享Yi系列模型，包括将Yi系列模型作
+为通过电子或其他远程方式（例如基于 API 或 Web 访问的任何 SaaS 软件或 PaaS 软
+件）提供。
+ 
+“商业用途”是指使用Yi系列模型，直接或间接为实体或个人进行运营、推广或产生收入，或
+用于任何其他盈利目的。
+ 
+“法律法规”是指中华人民共和国大陆地区（仅为本协议之目的，不包括香港、澳门和台湾）
+的法律及行政法规。
+ 
+“个人信息”是指以电子或者其他方式记录的与已识别或者可识别的自然人有关的各种信息，
+不包括匿名化处理后的信息。
+ 
+“标识” 是指任何商标、服务标记、商号、域名、网站名称或其他带有显著品牌特征的标记。
+ 
+ 
+2. 许可及许可限制
+
+许可方特此授予您非排他性、全球性、不可转让、不可再许可、可撤销、免版税的版权许可。
+您必须满足如下许可限制条件：
+ 
+1) 您对Yi系列模型的使用应遵守法律法规以及其他国家/地区适用的法律要求、尊重社会公
+德和伦理道德。包括但不限于您不得将Yi系列模型用作危害国家安全、宣扬恐怖主义、极端
+主义，宣扬民族及种族仇恨、歧视，暴力、色情，以及虚假有害信息等法律法规以及其他国
+家/地区适用的法律要求禁止的目的。
+ 
+2) 您不得出于军事或非法目的，或以法律法规以及其他国家/地区适用的法律要求所不允许
+的方式 a) 使用、复制、或分发Yi系列模型; 或 b) 创建Yi系列模型的全部或部分衍生品。
+ 
+3) 您对Yi系列模型的使用（包括使用Yi系列模型的输出）以及模型衍生品的创建不得侵犯
+任何第三方的合法权益，包括但不限于他人肖像权、名誉权、隐私权等人格权，著作权、专
+利权、商业秘密等知识产权，或其他财产权益。
+ 
+4) 您必须向Yi系列模型及Yi系列模型衍生品的任何第三方使用者明确Yi系列模型的来源为
+许可方并向其提供本协议的副本。
+ 
+5) 若您修改Yi系列模型得到模型衍生品，您必须以显著的方式说明修改的内容，且上述修
+改不得违反本协议的许可限制条件，也不能允许、协助或以其他方式使得第三方违反本协议
+中的许可限制条件。
+ 
+如果您计划将Yi系列模型及模型衍生品用作商业用途，请参见《Yi系列模型商用许可协议》
+（参见：https://www.lingyiwanwu.com/yi-license）附件一《Yi系列模型商用登
+记表》（“登记表”）并将填写完毕的登记表发送至 yi@01.ai 邮箱完成登记即可获得商用
+许可。若您获得商用许可并将Yi系列模型及模型衍生品用作商业用途，您应满足许可方上述
+许可限制条件及《Yi系列模型商用许可协议》中的商业许可限制。
+ 
+3. 知识产权
+
+Yi系列模型的所有权及其相关知识产权，由许可方单独所有。
+ 
+在任何情况下，未经许可方事先书面同意，您不得以任何方式使用许可方的任何标识。由于
+您违反本协议使用许可方的标识给许可方或他人造成损失的，由您承担全部法律责任。
+ 
+ 
+4. 免责声明及责任限制
+
+Yi系列模型按“原样”提供。许可方不对Yi系列模型提供任何明示或暗示的保证，包括但不限
+于：模型及输出结果的稳定性、所有权、适销性、非侵权性、或特定用途适用性。您将对适
+用、复制及分发Yi系列模型以及创建模型衍生品所产生的风险与后果承担所有责任。
+ 
+许可方在模型训练的所有阶段都遵守法律法规，坚持维护数据和算法的合法、真实、准确、
+客观和多样性。许可方不对您根据本协议使用、复制及分发Yi系列模型，以及创建模��衍生
+品而产生或与之相关的任何直接、间接、附带的后果、以及其他损失或损害承担责任。包括
+但不限于：
+ 
+1) 许可方不承担您因使用Yi系列模型而导致的数据安全风险。
+ 
+2) Yi系列模型中可能包含个人信息。在您使用Yi系列模型的过程中，您承认您为法律法规
+定义下决定个人信息处理方式和目的的个人信息处理者。您应遵守法律法规要求处理Yi系列
+模型中可能包含的个人信息，并承担相应的法律责任，以及处理个人信息的风险和后果。
+ 
+3) 许可方不承担您使用Yi系列模型或模型输出结果而产生的声誉风险。
+ 
+4) 许可方不承担您使用Yi系列模型的输出结果涉及的知识产权风险。
+ 
+若由于您对Yi系列模型的使用、复制或分发，或者创建模型衍生品而导致许可方遭受损失，
+许可方有权要求您对许可方的损失进行赔偿。对于任何第三方向许可方提出的因您使用、复
+制或分发Yi系列模型或创建模型衍生品行为的相关索赔，许可方有权要求您为许可方进行辩
+护、赔偿并使许可方免受损害。
+ 
+ 
+5. 争议解决
+
+协议的订立、效力、解释、履行、修改和终止，使用、复制和分发Yi系列模型以及争议解决
+均适用中华人民共和国大陆地区（仅为本协议之目的，不包括香港、澳门和台湾）法律，并
+排除冲突法的适用。
+ 
+因使用、复制和分发Yi系列模型而发生的任何争议，各方应首先通过友好协商的方式加以解
+决。协商不成时，应向许可方所在地人民法院提起诉讼。
+ 
+ 
+6. 协议的生效及终止
+
+您使用Yi系列模型即表示您已阅读并同意接受协议的约束。协议自您使用Yi系列模型之日起
+生效并将在您停止使用Yi系列模型之日起终止。若您违反协议中的任何条款或限制，许可方
+有权终止协议。
+ 
+若协议终止，您需立即停止使用Yi系列模型。本协议第4条“免责声明及责任限制”及第5条
+“争议解决”在协议终止后仍有效。
+ 
+ 
+7. 协议更新及联系方式
+
+许可方有权对协议进行不时更新。许可方将通过 https://01.ai 公布协议最新版本。有关
+许可和版权的任何问题，请通过 yi@01.ai 与许可方联系。

README.md

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+---
+license: other
+license_name: yi-license
+license_link: LICENSE
+datasets:
+- adamo1139/AEZAKMI_v2
+---
+
+## Model description
+
+Yi-34B 200K base model fine-tuned on AEZAKMI v2 dataset. Training took around 25 hours on single local RTX 3090 Ti.
+It's like airoboros but with less gptslop, no refusals and less typical language used by RLHFed OpenAI models.
+Say goodbye to  "It's important to remember"! \
+Prompt format is standard chatml. Don't expect it to be good at math, riddles or be crazy smart. My end goal with AEZAKMI is to create a cozy free chatbot.
+Cost of this fine-tune is about $10 in electricity. It took me 3 tries to get it right.
+Base model used for fine-tuning was 200k context Yi-34B-Llama model shared by larryvrh.
+
+I had to lower max_positional_embeddings in config.json and model_max_length for training to start, otherwise I was OOMing straight away. 
+My first attempt had max_positional_embeddings set to 16384 and model_max_length set to 200000. This allowed fine-tuning to finish, but that model was broken after applying LoRA and merging it. \
+This attempt had both max_position_embeddings and model_max_length set to 4096, which worked perfectly fine.
+
+## Prompt Format
+
+I recommend using ChatML format, as this was used during fine-tune. \
+Here's a prompt format you should use, you can set a different system message, model seems to respect that fine, so it wasn't overfitted.
+
+```
+<|im_start|>system
+A chat with uncensored assistant.<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant
+```
+
+## Intended uses & limitations
+
+Use is limited by Yi license.
+
+## Known Issues
+
+I recommend to set repetition penalty to something around 1.05 to avoid repetition. So far I had good experience running this model with temperature 1.2.
+Stories have ChatGPT like paragraph spacing, I will work on this in the future maybe, not a high priority
+
+Unrestricted-ness of the v2 isn't quite something that I am happy with yet, especially using prompt "A chat.". \
+With a slightly modifed prompt it works much better, I recommend using "A chat with uncensored assistant.", it's stupid but it helps a lot. \
+Base Yi-34B model is contaminated with refusals and this contaminates all models trained on Yi-34B \
+My next project is to attempt to de-contaminate base Yi-34B 4K and Yi-34B 200K using DPO with preferred data coming from uncontaminated raw models. I plan to release that dataset openly.
+
+I was made aware of the frequent occurrence of the phrase "sending shivers down a spine" in the generations during RP of v1, so I fixed those samples - it should be better now. \
+I can hold up to 24000 ctx with 4.65bpw exl2 version and 8-bit cache - long context should work as good as other models trained on 200k version of Yi-34B \
+There is also some issue with handling long system messages for RP, I was planning to investigate it for v2 but I didn't.
+
+
+## Axolotl training parameters
+
+- bnb_4bit_use_double_quant: true
+- is_llama_derived_model: true
+- load_in_4bit: true
+- adapter: qlora
+- sequence_len: 1400
+- sample_packing: true
+- lora_r: 16
+- lora_alpha: 32
+- lora_target_modules:
+  - q_proj
+  - v_proj
+  - k_proj
+  - o_proj
+  - gate_proj
+  - down_proj
+  - up_proj
+ - lora_target_linear: true
+ - pad_to_sequence_len: false
+ - micro_batch_size: 1
+ - gradient_accumulation_steps: 1
+ - num_epochs: 2.4
+ - optimizer: adamw_bnb_8bit
+ - lr_scheduler: constant
+ - learning_rate: 0.00005
+ - train_on_inputs: false
+ - group_by_length: false
+ - bf16: true
+ - bfloat16: true
+ - flash_optimum: false
+ - gradient_checkpointing: true
+ - flash_attention: true
+ - seed: 42
+
+
+## Upcoming
+
+I will probably be working on de-contaminating base Yi-34B model now. \
+My second run of AEZAKMI v2 fine-tune was just 0.15 epochs and I really like how natural this model is and how rich is it's vocabulary. I will try to train less to hit the sweetspot. \
+I will be uploading LoRA adapter for that second run that was just 0.15 epochs. \
+I believe that I might have gotten what I want if I would have stopped training sooner. I don't have checkpoints older than 1500 steps back so I would need to re-run training to get it back.

config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 7168,
+  "initializer_range": 0.02,
+  "intermediate_size": 20480,
+  "max_position_embeddings": 200000,
+  "model_type": "llama",
+  "num_attention_heads": 56,
+  "num_hidden_layers": 60,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 5000000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.34.0",
+  "use_cache": true,
+  "vocab_size": 64000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "pad_token_id": 0,
+  "transformers_version": "4.34.0"
+}

model.safetensors.index.json

@@ -0,0 +1,550 @@
+{
+  "metadata": {
+    "total_size": 68777834496
+  },
+  "weight_map": {
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