--- 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 @MarinaraSpaghetti has extensively tested the model and recommended the following settings. They seem to work quite well: ``` { "temp": 1, "temperature_last": true, "top_p": 1, "top_k": 0, "top_a": 0, "tfs": 1, "epsilon_cutoff": 0, "eta_cutoff": 0, "typical_p": 0.9, "min_p": 0, "rep_pen": 1.1, "rep_pen_range": 19456, "no_repeat_ngram_size": 0, "penalty_alpha": 0, "num_beams": 1, "length_penalty": 0, "min_length": 0, "encoder_rep_pen": 1, "freq_pen": 0, "presence_pen": 0, "do_sample": true, "early_stopping": false, "dynatemp": false, "min_temp": 1, "max_temp": 2, "dynatemp_exponent": 1, "smoothing_factor": 0.33, "add_bos_token": false, "truncation_length": 2048, "ban_eos_token": false, "skip_special_tokens": true, "streaming": true, "mirostat_mode": 0, "mirostat_tau": 5, "mirostat_eta": 0.1, "guidance_scale": 1, "negative_prompt": "", "grammar_string": "", "banned_tokens": "", "ignore_eos_token_aphrodite": false, "spaces_between_special_tokens_aphrodite": true, "sampler_order": [ 6, 0, 1, 3, 4, 2, 5 ], "logit_bias": [], "n": 1, "rep_pen_size": 0, "genamt": 400, "max_length": 38912 } ``` 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