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.gitattributes

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+Yi-34B-200K-RPMerge_Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
+Yi-34B-200K-RPMerge_Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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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
+base_model: []
+tags:
+- mergekit
+- merge
+- Yi
+- exllama
+- exllamav2
+- exl2
+---
+# RPMerge
+A merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling.
+
+Disappointed with some quirks of my previous kitchen sink merges (like token/instruct formats from various models showing up when they shouldn't), I've gone 'back to the basics' and picked a few Vicuna-format only models:
+
+- [DrNicefellow/ChatAllInOne-Yi-34B-200K-V1](https://huggingface.co/DrNicefellow/ChatAllInOne-Yi-34B-200K-V1) and [migtissera/Tess-34B-v1.5b](https://huggingface.co/migtissera/Tess-34B-v1.5b) both have excellent general instruction-following performance.
+
+- [cgato/Thespis-34b-v0.7](https://huggingface.co/cgato/Thespis-34b-v0.7) is trained on the "Username: {Input} / BotName: {Response}" format, to emphasize it in the merge (but not force it). It also seems to work for multi-character stories.
+
+- [Doctor-Shotgun/limarpv3-yi-llama-34b-lora](https://huggingface.co/Doctor-Shotgun/limarpv3-yi-llama-34b-lora) is trained on roleplaying data, but merged at a modest weight to not over emphasize it. This is the only non-vicuna model (being alpaca format), but it doesn't seem to interefere with the Vicuna format or adversely affect long-context perplexity
+
+- [adamo1139/yi-34b-200k-rawrr-dpo-2](https://huggingface.co/adamo1139/yi-34b-200k-rawrr-dpo-2) the base for the limarp lora, this is base Yi gently finetuned to discourage refusals.
+
+- [migtissera/Tess-M-Creative-v1.0](https://huggingface.co/migtissera/Tess-M-Creative-v1.0) and [NousResearch/Nous-Capybara-34B](https://huggingface.co/NousResearch/Nous-Capybara-34B) are both "undertrained" Yi models. I find they excel at raw completion performance (like long novel continuations) while still retaining some Vicuna instruct ability. This may be why some still prefer the original Tess 1.0/Capybara merge.
+
+I consider this a more "focused" merge that previous ones. I will investigate other models (perhaps chatML models?) for a more "factual assistant" focused merge, as well as a coding-focused merge if I can't find one to suit my needs.
+
+
+## Prompt template: Orca-Vicuna
+```
+SYSTEM: {system_message}
+USER: {prompt}
+ASSISTANT:
+```
+Raw prompting as described here is also effective: https://old.reddit.com/r/LocalLLaMA/comments/18zqy4s/the_secret_to_writing_quality_stories_with_llms/
+
+As well as a very explicit system prompt like this: https://old.reddit.com/r/LocalLLaMA/comments/1aiz6zu/roleplaying_system_prompts/koygiwa/
+
+
+## Running
+
+Chinese models with large tokenizer vocabularies like Yi need *careful* parameter tuning due to their huge logit sampling "tails." Yi in particular also runs relatively "hot" even at lower temperatures.
+
+I am a huge fan of Kalomaze's quadratic sampling (shown as "smoothing factor" where available), as described here: https://github.com/oobabooga/text-generation-webui/pull/5403
+
+Otherwise, I recommend a lower temperature with 0.1 or higher MinP, a little repetition penalty, and mirostat with a low tau, and no other samplers. See the explanation here: https://github.com/ggerganov/llama.cpp/pull/3841
+
+24GB GPUs can efficiently run Yi-34B-200K models at **40K-90K context** with exllamav2, and performant UIs like [exui](https://github.com/turboderp/exui). 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/). Empty 16GB GPUs can still run the high context with aggressive quantization.
+
+To load/train this in full-context backends like transformers, you *must* change `max_position_embeddings` in config.json to a lower value than 200,000, otherwise you will OOM! I do not recommend running high context without context-efficient backends that support flash attention + 8 bit kv cache, like exllamav2, litellm, vllm or unsloth.
+
+
+## Testing Notes
+
+Thanks to ParasiticRogue for this idea of a Vicuna-only merge, see: https://huggingface.co/brucethemoose/jondurbin_bagel-dpo-34b-v0.2-exl2-4bpw-fiction/discussions
+
+See: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-megamerge-v8#testing-notes
+
+This is a possible base for a storytelling finetune/LASER in the future, once I can bite the bullet and rent some A100s or a MI300. 
+
+I have tested this merge with with novel-style continuation (but not much chat-style roleplay), and some assistant-style responses and long context analysis. I haven't seen any refusals so far.
+
+## Merge Details
+### Merge Method
+
+This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama as a base.
+
+### Models Merged
+
+The following models were included in the merge:
+* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b
+* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0
+* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7
+* /home/alpha/Models/Raw/Nous-Capybara-34B
+* /home/alpha/Models/Raw/admo_limarp
+* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1
+
+### Configuration
+
+The following YAML configuration was used to produce this model:
+
+```yaml
+models:
+  - model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama
+    # No parameters necessary for base model
+  - model: /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b
+    #Emphasize the beginning of Vicuna format models
+    parameters:
+      weight: 0.19
+      density: 0.59
+  - model: /home/alpha/Models/Raw/Nous-Capybara-34B
+    parameters:
+      weight: 0.19
+      density: 0.55
+  # Vicuna format
+  - model: /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0
+    parameters:
+      weight: 0.05
+      density: 0.55
+  - model: /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1
+    parameters:
+      weight: 0.19
+      density: 0.55
+  - model: adamo1139/yi-34b-200k-rawrr-dpo-2+Doctor-Shotgun/limarpv3-yi-llama-34b-lora
+    parameters:
+      weight: 0.19
+      density: 0.48
+  - model: /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7
+    parameters:
+      weight: 0.19
+      density: 0.59
+
+
+merge_method: dare_ties
+tokenizer_source: union
+base_model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama
+parameters:
+  int8_mask: true
+dtype: bfloat16
+```
+
+
+## Self Promotion
+
+I'm part of a AI startup called Holocene AI!
+
+We're new, busy, and still setting things up. But if you have any business inquiries, want a job, or just want some consultation, feel free to shoot me an email. We have expertise in RAG applications and llama/embeddings model finetuning, and absolutely *none* of the nonsense of scammy AI startups.
+
+Contact me at: agates.holocene.ai@gmail.com
+
+I also set up a Ko-Fi! I want to run some (personal) training/LASERing as well, at 100K context or so. If you'd like to buy me 10 minutes on an A100 (or 5 seconds on an MI300X), I'd appreciate it: https://ko-fi.com/alphaatlas
+
+***
+
+Vanilla Quantization by [nold](https://huggingface.co/nold), Original Model [brucethemoose/Yi-34B-200K-RPMerge](https://huggingface.co/brucethemoose/Yi-34B-200K-RPMerge). Created using [llm-quantizer](https://github.com/Nold360/llm-quantizer) Pipeline - 0e95dcd401087b713c2eca7c89ff8108e61969f0 

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