license: cc-by-4.0
tags:
- gguf
- mistral
- roleplay
Quants:
quantization_options = [
"Q4_K_M", "Q4_K_S", "IQ4_XS", "Q5_K_M", "Q5_K_S",
"Q6_K", "Q8_0", "IQ3_M", "IQ3_S", "IQ3_XXS"
]
This repository hosts GGUF-IQ-Imatrix quants for SanjiWatsuki/Sonya-7B.
What does "Imatrix" mean?
It stands for Importance Matrix, a technique used to improve the quality of quantized models. The Imatrix is calculated based on calibration data, and it helps determine the importance of different model activations during the quantization process. The idea is to preserve the most important information during quantization, which can help reduce the loss of model performance, especially when the calibration data is diverse. [1] [2]
For imatrix data generation, kalomaze's groups_merged.txt with added roleplay chats was used, you can find it here. This was just to add a bit more diversity to the data.
Steps:
Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants)
Using the latest llama.cpp at the time.
Original model information:
Top 1 Performer MT-bench 🤪
WTF is This?
Sonya-7B is, at the time of writing, the #1 performing model in MT-Bench first turn, ahead of GPT-4, and overall the #2 model in MT-Bench, to the best of my knowledge. Sonya-7B should be a good all-purpose model for all tasks including assistant, RP, etc.
Sonya-7B has a similar structure to my previous model, Silicon-Maid-7B, and uses a very similar merge. It's a merge of xDAN-AI/xDAN-L1-Chat-RL-v1, Jan-Ai's Stealth v1.2, chargoddard/piano-medley-7b, NeverSleep/Noromaid-7B-v0.2, and athirdpath/NSFW_DPO_vmgb-7b. Sauce is below. Somehow, by combining these pieces, it substantially outscores any of its parents on MT-Bench.
I picked these models because:
- MT-Bench normally correlates well with real world model quality and xDAN performs well on it.
- Almost all models in the mix were Alpaca prompt formatted which gives prompt consistency.
- Stealth v1.2 has been a magic sprinkle that seems to increase my MT-Bench scores.
- I added RP models because it boosted the Writing and Roleplay benchmarks 👀
Based on the parent models, I expect this model to be used with an 8192 context window. Please use NTK scaling alpha of 2.6 to experimentally try out 16384 context.
Let me be candid: Despite the test scores, this model is NOT is a GPT killer. I think it's a very sharp model for a 7B, it probably punches way above its weight for a 7B, but it's still a 7B model. Even for a 7B model, I think it's quirky and has some weird outputs, probably due to how Frankenstein this merge is. Keep your expectations in check 😉
MT-Bench Average Turn
| model | score | size |
|---|---|---|
| gpt-4 | 8.99 | - |
| Sonya-7B | 8.52 | 7b |
| xDAN-L1-Chat-RL-v1 | 8.34 | 7b |
| Starling-7B | 8.09 | 7b |
| Claude-2 | 8.06 | - |
| Silicon-Maid | 7.96 | 7b |
| Loyal-Macaroni-Maid | 7.95 | 7b |
| gpt-3.5-turbo | 7.94 | 20b? |
| Claude-1 | 7.90 | - |
| OpenChat-3.5 | 7.81 | - |
| vicuna-33b-v1.3 | 7.12 | 33b |
| wizardlm-30b | 7.01 | 30b |
| Llama-2-70b-chat | 6.86 | 70b |
The Sauce
models:
- model: xDAN-AI/xDAN-L1-Chat-RL-v1
parameters:
weight: 1
density: 1
- model: chargoddard/piano-medley-7b
parameters:
weight: 0.3
- model: jan-hq/stealth-v1.2
parameters:
weight: 0.2
- model: NeverSleep/Noromaid-7b-v0.2
parameters:
weight: 0.2
- model: athirdpath/NSFW_DPO_vmgb-7b
parameters:
weight: 0.2
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
density: 0.4
int8_mask: true
normalize: true
dtype: bfloat16
There was no additional training, finetuning, or DPO. This is a straight merger.
Prompt Template (Alpaca)
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
I found that this model performed worse with the xDAN prompt format so, despite the heavy weight of xDAN in this merger, I recommeend against its use.
Other Benchmark Stuff
########## First turn ##########
| model | turn | score | size |
|---|---|---|---|
| Sonya-7B | 1 | 9.06875 | 7b |
| gpt-4 | 1 | 8.95625 | - |
| xDAN-L1-Chat-RL-v1 | 1 | 8.87500 | 7b |
| xDAN-L2-Chat-RL-v2 | 1 | 8.78750 | 30b |
| claude-v1 | 1 | 8.15000 | - |
| gpt-3.5-turbo | 1 | 8.07500 | 20b |
| vicuna-33b-v1.3 | 1 | 7.45625 | 33b |
| wizardlm-30b | 1 | 7.13125 | 30b |
| oasst-sft-7-llama-30b | 1 | 7.10625 | 30b |
| Llama-2-70b-chat | 1 | 6.98750 | 70b |
########## Second turn ##########
| model | turn | score | size |
|---|---|---|---|
| gpt-4 | 2 | 9.025000 | - |
| xDAN-L2-Chat-RL-v2 | 2 | 8.087500 | 30b |
| Sonya-7B | 2 | 7.962500 | 7b |
| xDAN-L1-Chat-RL-v1 | 2 | 7.825000 | 7b |
| gpt-3.5-turbo | 2 | 7.812500 | 20b |
| claude-v1 | 2 | 7.650000 | - |
| wizardlm-30b | 2 | 6.887500 | 30b |
| vicuna-33b-v1.3 | 2 | 6.787500 | 33b |
| Llama-2-70b-chat | 2 | 6.725000 | 70b |
If you'd like to replicate the MT-Bench run, please ensure that the Alpaca prompt template is applied to the model. I did this by putting "alpaca" in the model path to trigger the AlpacaAdapter.