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--- |
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license: other |
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license_name: yi-license |
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license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE |
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language: |
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- en |
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library_name: transformers |
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base_model: [] |
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tags: |
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- mergekit |
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- merge |
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- Yi |
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- exllama |
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- exllamav2 |
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- exl2 |
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--- |
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# RPMerge |
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A merge of several Yi 34B models with a singular goal: 40K+ context, instruct-enhanced storytelling. |
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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: |
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- [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. |
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- [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. |
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- [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 |
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- [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. |
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- [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. |
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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. |
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## Prompt template: Orca-Vicuna |
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``` |
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SYSTEM: {system_message} |
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USER: {prompt} |
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ASSISTANT: |
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``` |
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Raw prompting as described here is also effective: https://old.reddit.com/r/LocalLLaMA/comments/18zqy4s/the_secret_to_writing_quality_stories_with_llms/ |
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As well as a very explicit system prompt like this: https://old.reddit.com/r/LocalLLaMA/comments/1aiz6zu/roleplaying_system_prompts/koygiwa/ |
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## Running |
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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. |
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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 |
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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 |
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@MarinaraSpaghetti has extensively tested the model and recommended the following settings. They seem to work quite well: |
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``` |
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{ |
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"temp": 1, |
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"temperature_last": true, |
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"top_p": 1, |
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"top_k": 0, |
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"top_a": 0, |
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"tfs": 1, |
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"epsilon_cutoff": 0, |
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"eta_cutoff": 0, |
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"typical_p": 0.9, |
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"min_p": 0, |
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"rep_pen": 1.1, |
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"rep_pen_range": 19456, |
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"no_repeat_ngram_size": 0, |
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"penalty_alpha": 0, |
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"num_beams": 1, |
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"length_penalty": 0, |
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"min_length": 0, |
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"encoder_rep_pen": 1, |
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"freq_pen": 0, |
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"presence_pen": 0, |
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"do_sample": true, |
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"early_stopping": false, |
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"dynatemp": false, |
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"min_temp": 1, |
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"max_temp": 2, |
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"dynatemp_exponent": 1, |
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"smoothing_factor": 0.33, |
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"add_bos_token": false, |
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"truncation_length": 2048, |
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"ban_eos_token": false, |
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"skip_special_tokens": true, |
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"streaming": true, |
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"mirostat_mode": 0, |
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"mirostat_tau": 5, |
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"mirostat_eta": 0.1, |
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"guidance_scale": 1, |
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"negative_prompt": "", |
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"grammar_string": "", |
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"banned_tokens": "", |
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"ignore_eos_token_aphrodite": false, |
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"spaces_between_special_tokens_aphrodite": true, |
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"sampler_order": [ |
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6, |
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0, |
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1, |
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3, |
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4, |
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2, |
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5 |
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], |
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"logit_bias": [], |
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"n": 1, |
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"rep_pen_size": 0, |
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"genamt": 400, |
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"max_length": 38912 |
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} |
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``` |
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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. |
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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. |
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## Testing Notes |
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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 |
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See: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-megamerge-v8#testing-notes |
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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. |
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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. |
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## Merge Details |
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### Merge Method |
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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. |
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### Models Merged |
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The following models were included in the merge: |
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* /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b |
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* /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0 |
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* /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7 |
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* /home/alpha/Models/Raw/Nous-Capybara-34B |
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* /home/alpha/Models/Raw/admo_limarp |
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* /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1 |
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### Configuration |
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The following YAML configuration was used to produce this model: |
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```yaml |
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models: |
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- model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama |
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# No parameters necessary for base model |
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- model: /home/alpha/Models/Raw/migtissera_Tess-34B-v1.5b |
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#Emphasize the beginning of Vicuna format models |
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parameters: |
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weight: 0.19 |
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density: 0.59 |
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- model: /home/alpha/Models/Raw/Nous-Capybara-34B |
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parameters: |
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weight: 0.19 |
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density: 0.55 |
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# Vicuna format |
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- model: /home/alpha/Models/Raw/migtissera_Tess-M-Creative-v1.0 |
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parameters: |
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weight: 0.05 |
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density: 0.55 |
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- model: /home/alpha/Models/Raw/DrNicefellow_ChatAllInOne-Yi-34B-200K-V1 |
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parameters: |
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weight: 0.19 |
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density: 0.55 |
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- model: adamo1139/yi-34b-200k-rawrr-dpo-2+Doctor-Shotgun/limarpv3-yi-llama-34b-lora |
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parameters: |
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weight: 0.19 |
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density: 0.48 |
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- model: /home/alpha/Models/Raw/cgato_Thespis-34b-DPO-v0.7 |
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parameters: |
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weight: 0.19 |
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density: 0.59 |
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merge_method: dare_ties |
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tokenizer_source: union |
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base_model: /home/alpha/Models/Raw/chargoddard_Yi-34B-200K-Llama |
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parameters: |
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int8_mask: true |
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dtype: bfloat16 |
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``` |
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## Self Promotion |
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I'm part of a AI startup called Holocene AI! |
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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. |
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Contact me at: agates.holocene.ai@gmail.com |
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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 |
