Quantization made by Richard Erkhov.
Yi-34B-200K-DARE-merge-v5 - GGUF
- Model creator: https://huggingface.co/brucethemoose/
- Original model: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v5/
Original model description:
language: - en license: other library_name: transformers tags: - text-generation-inference - merge license_name: yi-license license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE pipeline_tag: text-generation model-index: - name: Yi-34B-200K-DARE-merge-v5 results: - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 66.47 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-merge-v5 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 85.54 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-merge-v5 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 77.22 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-merge-v5 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 57.46 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-merge-v5 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 82.24 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-merge-v5 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 62.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=brucethemoose/Yi-34B-200K-DARE-merge-v5 name: Open LLM Leaderboard
Succeeded by a new merge: https://huggingface.co/brucethemoose/Yi-34B-200K-DARE-merge-v7
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:
Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch
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 0.02-0.1 MinP, a little repetition penalty, and no other samplers. Yi tends to run "hot" by default, and it really needs MinP to cull the huge vocabulary.
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
I recommend exl2 quantizations profiled on data similar to the desired task. It is especially sensitive to the quantization data at low bpw. I've published my own fiction-oriented quantizations here: https://huggingface.co/collections/brucethemoose/most-recent-merge-65742644ca03b6c514afa204
To load 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!
Testing Notes
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
Various densities were tested with perplexity tests and long context prompts. Relatively high densities seem to perform better, contrary to the findings of the Super Mario paper.
This particular version is merged with more than the "recommended" max density of 0.5. It seems to result in even better perplexity, but I'm not sure if this translates to better output.
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.
SUS Chat 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 SUS's performance.
Dolphin 200K was taken out of this merge because it seems to be performing poorly for a 34B Dolphin model, like something went wrong during training?
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.
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
https://huggingface.co/chargoddard/Yi-34B-200K-Llama
https://huggingface.co/01-ai/Yi-34B-200K
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 71.98 |
| AI2 Reasoning Challenge (25-Shot) | 66.47 |
| HellaSwag (10-Shot) | 85.54 |
| MMLU (5-Shot) | 77.22 |
| TruthfulQA (0-shot) | 57.46 |
| Winogrande (5-shot) | 82.24 |
| GSM8k (5-shot) | 62.93 |
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