Nous-Capybara-34B, Tess-M-v1.4, Airoboros-3_1-yi-34b-200k, PlatYi-34B-200K-Q, Pallas-0.4, Yi-34B-200K-AEZAKMI-v2, and a tiny bit of SUS-Chat-34B merged with a new, experimental implementation of "dare ties" via mergekit.
See the main model card: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v5
The merge was then quantized with exllamav2's 0.0.11 brand new exl2 quantization, using 300K tokens from a sci fi story, a fantasy story, and a Vicuna format chat as profiling data, at a high context size. This should results in excellent writing performance for the model size.
This 2.67bpw quantization can fit Long Context on a 16GB GPU at usable quality.
Prompt template: Orca-Vicuna
SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
It might recognize ChatML, or maybe 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 running a lower temperature with 0.05-0.1 MinP, a little repitition 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, and performant UIs like exui. I go into more detail in this post
Commands
First pass:
python convert.py --in_dir /home/alpha/FastModels/Yi-34B-200K-DARE-merge-v5 -o /home/alpha/FastModels/scratch -om /home/alpha/FastModels/v5.json --cal_dataset /home/alpha/Documents/smol.parquet -ml 32768 -mr 9 -ss 4096 -b 4.0 -hb 6 -nr
Second pass:
python convert.py --in_dir /home/alpha/FastModels/Yi-34B-200K-DARE-merge-v5 -o /home/alpha/FastModels/scratch -m /home/alpha/FastModels/v5.json --cal_dataset /home/alpha/Documents/medium.parquet -l 12288 -r 29 -ml 32768 -mr 9 -ss 4096 -b 2.67 -hb 6 -cf /home/alpha/FastModels/Yi-34B-200K-DARE-merge-v5-exl2-2.67bpw-fiction -nr
Merged in mergekit 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
# Less weight than previous merge since Pallas is a finetune of Tess
parameters:
weight: 0.14
density: 0.62
- model: /home/alpha/FastModels/Mihaiii_Pallas-0.4
parameters:
weight: 0.14
density: 0.62
- model: /home/alpha//Storage/Models/Raw/bhenrym14_airoboros-3_1-yi-34b-200k
parameters:
weight: 0.14
density: 0.52
- model: /home/alpha/Storage/Models/Raw/Nous-Capybara-34B
parameters:
weight: 0.22
density: 0.62
- model: /home/alpha/Storage/Models/Raw/kyujinpy_PlatYi-34B-200k-Q-FastChat
parameters:
weight: 0.14
density: 0.52
#- model: /home/alpha/Storage/Models/Raw/ehartford_dolphin-2.2-yi-34b-200k
# Dolphin 200K seems to be broken according to multiple leaderboards and perplexity tests?
# parameters:
# weight: 0.15
# density: 0.6
- model: /home/alpha/Models/Raw/adamo1139_Yi-34B-200K-AEZAKMI-v2
parameters:
weight: 0.14
density: 0.52
- model: /home/alpha/Models/Raw/SUSTech_SUS-Chat-34B/
# Very low density and low weight since its a Yi 4K finetune, to try and preserve long context performance while "keeping" some of SUS
parameters:
weight: 0.08
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
Credits:
https://github.com/cg123/mergekit/tree/dare
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/kyujinpy/PlatYi-34B-200k-Q-FastChat
https://huggingface.co/adamo1139/Yi-34B-200K-AEZAKMI-v2
https://huggingface.co/Mihaiii/Pallas-0.4
https://huggingface.co/SUSTech/SUS-Chat-34B
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