Create README.md

README.md

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+---
+license: other
+license_name: yi-license
+license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- text-generation-inference
+---
+
+**Dolphin-2.2-yi-34b-200k**, **Nous-Capybara-34B**, **Tess-M-v1.4**, **Airoboros-3_1-yi-34b-200k**, **PlatYi-34B-200K-Q**, and **Una-xaberius-34b-v1beta** merged with a new, experimental implementation of "dare ties" via mergekit.
+
+Quantized with the git version of exllamav2 with 200 rows (400K tokens) on a long Orca-Vicuna format chat, a selected sci fi story and a fantasy story. This should hopefully yield better chat/storytelling performance than the short, default wikitext quantization.
+
+4bpw is enough for **~47K context on a 24GB GPU.** I would highly recommend running in exui for speed at long context. I go into more detail in this [Reddit post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/)
+
+Merged with the following config, and the tokenizer from chargoddard's Yi-Llama:
+```
+models:
+  - model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
+    # no parameters necessary for base model
+  - model: /home/alpha/Storage/Models/Raw/migtissera_Tess-34B-v1.4
+    parameters:
+      weight: 0.19
+      density: 0.44
+  - model: /home/alpha//Storage/Models/Raw/bhenrym14_airoboros-3_1-yi-34b-200k
+    parameters:
+      weight: 0.14
+      density: 0.34
+  - model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B
+    parameters:
+      weight: 0.19
+      density: 0.44
+  - model: /home/alpha/Storage/Models/Raw/kyujinpy_PlatYi-34B-200K-Q
+    parameters:
+      weight: 0.14
+      density: 0.34
+  - model: /home/alpha/FastModels/ehartford_dolphin-2.2-yi-34b-200k
+    parameters:
+      weight: 0.19
+      density: 0.44
+  - model: /home/alpha/FastModels/fblgit_una-xaberius-34b-v1beta
+    parameters:
+      weight: 0.15
+      density: 0.08
+merge_method: dare_ties
+base_model: /home/alpha/Storage/Models/Raw/chargoddard_Yi-34B-200K-Llama
+parameters:
+
+int8_mask: true
+dtype: bfloat16
+
+```
+
+First exllama quantization pass:
+```
+python convert.py --in_dir //home/alpha/FastModels/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties -o /home/alpha/FastModels/scratch -om /home/alpha/FastModels/mes.json --cal_dataset /home/alpha/Documents/smol.parquet -l 2048 -r 80 -ml 2048 -mr 40 -gr 40 -ss 4096 -nr -b 4.0 -hb 6
+```
+
+Second exllama quantization pass:
+```
+python convert.py --in_dir /home/alpha/FastModels/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties -o /home/alpha/FastModels/scratch -m /home/alpha/FastModels/mes.json --cal_dataset /home/alpha/Documents/medium.parquet -l 2048 -r 200 -ml 2048 -mr 40 -gr 200 -ss 4096 -b 4.0 -hb 6 -cf /home/alpha/FastModels/CaPlatTessDolXaBoros-Yi-34B-200K-DARE-Ties-exl2-4bpw-fiction -nr
+```
+## Testing Notes
+
+Various densities were tested with perplexity tests and high context prompts. Relatively high densities seem to perform better, contrary to the findings of the Super Mario paper.
+
+Weights that add up to 1 seems to be optimal.
+
+Dare Ties is also resulting in seemingly better, lower perplexity merges than a regular ties merge, task arithmetic or a slerp merge.
+
+Xaberuis is not a 200K model, hence it was merged at a very low density to try and preserve Yi 200K's long context performance while still inheriting some of Xaberius's performance. 
+
+I chose not to include other finetunes because they aren't trained on the 200K base. If any other 200K finetunes pop up, let me know.
+
+***
+## Prompt template: Orca-Vicuna?
+```
+SYSTEM: {system_message}
+USER: {prompt}
+ASSISTANT:
+```
+It might recognize ChatML from Dolphin+Xaberius, and Llama-chat from Airoboros.
+
+Sometimes the model "spells out" the stop token as `</s>` like Capybara, so you may need to add `</s>` as an additional stopping condition.
+
+***
+## Running
+Being a Yi model, try disabling the BOS token and/or running a lower temperature with 0.05-0.13 MinP, a little repetition penalty, and no other samplers. Yi tends to run "hot" by default.
+
+24GB GPUs can run Yi-34B-200K models at **45K-75K context** with exllamav2. I go into more detail in this [post](https://old.reddit.com/r/LocalLLaMA/comments/1896igc/how_i_run_34b_models_at_75k_context_on_24gb_fast/)
+
+I recommend exl2 quantizations profiled on data similar to the desired task. It is especially sensitive to the quantization data at low bpw!
+
+To load this in full-context backends like transformers and vllm, you *must* change `max_position_embeddings` in config.json to a lower value than 200,000, otherwise you will OOM! 
+
+***
+
+## Credits:
+https://github.com/turboderp/exllamav2
+
+https://github.com/cg123/mergekit/tree/dare
+
+https://huggingface.co/ehartford/dolphin-2.2-yi-34b-200k
+
+https://huggingface.co/kyujinpy/PlatYi-34B-200K-Q
+
+https://huggingface.co/NousResearch/Nous-Capybara-34B/
+
+https://huggingface.co/bhenrym14/airoboros-3_1-yi-34b-200k
+
+https://huggingface.co/migtissera/Tess-M-v1.4
+
+https://huggingface.co/fblgit/una-xaberius-34b-v1beta
+
+https://huggingface.co/chargoddard/Yi-34B-200K-Llama
+
+https://huggingface.co/01-ai/Yi-34B-200K