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RichardErkhov/Undi95_-_Mistral-11B-OmniMix-gguf
Undi95_-_Mistral-11B-OmniMix-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
334
13% of all-time downloads
All-time downloads
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.gguf141 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
Mistral-11B-OmniMix - GGUF
| Name | Quant method | Size |
|---|---|---|
| Mistral-11B-OmniMix.Q2_K.gguf | Q2_K | 3.73GB |
| Mistral-11B-OmniMix.IQ3_XS.gguf | IQ3_XS | 4.14GB |
| Mistral-11B-OmniMix.IQ3_S.gguf | IQ3_S | 4.37GB |
| Mistral-11B-OmniMix.Q3_K_S.gguf | Q3_K_S | 4.34GB |
| Mistral-11B-OmniMix.IQ3_M.gguf | IQ3_M | 4.51GB |
| Mistral-11B-OmniMix.Q3_K.gguf | Q3_K | 4.84GB |
| Mistral-11B-OmniMix.Q3_K_M.gguf | Q3_K_M | 4.84GB |
| Mistral-11B-OmniMix.Q3_K_L.gguf | Q3_K_L | 5.26GB |
| Mistral-11B-OmniMix.IQ4_XS.gguf | IQ4_XS | 5.43GB |
| Mistral-11B-OmniMix.Q4_0.gguf | Q4_0 | 5.66GB |
| Mistral-11B-OmniMix.IQ4_NL.gguf | IQ4_NL | 5.72GB |
| Mistral-11B-OmniMix.Q4_K_S.gguf | Q4_K_S | 5.7GB |
| Mistral-11B-OmniMix.Q4_K.gguf | Q4_K | 6.02GB |
| Mistral-11B-OmniMix.Q4_K_M.gguf | Q4_K_M | 6.02GB |
| Mistral-11B-OmniMix.Q4_1.gguf | Q4_1 | 6.27GB |
| Mistral-11B-OmniMix.Q5_0.gguf | Q5_0 | 6.89GB |
| Mistral-11B-OmniMix.Q5_K_S.gguf | Q5_K_S | 6.89GB |
| Mistral-11B-OmniMix.Q5_K.gguf | Q5_K | 7.08GB |
| Mistral-11B-OmniMix.Q5_K_M.gguf | Q5_K_M | 7.08GB |
| Mistral-11B-OmniMix.Q5_1.gguf | Q5_1 | 7.51GB |
| Mistral-11B-OmniMix.Q6_K.gguf | Q6_K | 8.2GB |
| Mistral-11B-OmniMix.Q8_0.gguf | Q8_0 | 10.62GB |
I FUCKED UP, THIS MODEL IS MEANT TO BE A BFLOAT16 MODEL, I'M CURRENTLY REDOING IT IN THE CORRECT WAY (look at the recipe, it end in float16, i'm so dumb lmao). It SHOULD be even better, I saw the problem after finetuning it, something was off. It's usable, it rank the best, but it's not even on the right float...KEK
Fixed model should be here: NeverSleep/Mistral-11B-OmniMix-bf16
Don't mind this one at the moment, I need to finetune it for RP, it's just a test.
This repo contains fp16 files of Mistral-11B-OmniMix.
My goal for this model was only to make it score the highest possible with merge and layer toying, proving that:
The best one after further testing is this one:
<|system|>
Below is an instruction that describes a task. Write a response that appropriately completes the request.
<|user|>
{prompt}
<|assistant|>

But these one work too:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
USER: <prompt>
ASSISTANT:
Or use any prompting system from one of the 4 source model, should work.
Mistral-11B-OpenOrcaPlatypus :
slices:
- sources:
- model: Open-Orca/Mistral-7B-OpenOrca
layer_range: [0, 24]
- sources:
- model: akjindal53244/Mistral-7B-v0.1-Open-Platypus
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
Mistral-11B-CC-Zephyr :
slices:
- sources:
- model: "/content/drive/MyDrive/CC-v1.1-7B-bf16"
layer_range: [0, 24]
- sources:
- model: "/content/drive/MyDrive/Zephyr-7B"
layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16
Mistral-11B-OmniMix :
slices:
- sources:
- model: Mistral-11B-OpenOrcaPlatypus
layer_range: [0, 48]
- model: Mistral-11B-CC-Zephyr
layer_range: [0, 48]
merge_method: slerp
base_model: Undi95/Mistral-11B-OpenOrcaPlatypus
parameters:
t:
- filter: lm_head
value: [0.75]
- filter: embed_tokens
value: [0.75]
- filter: self_attn
value: [0.75, 0.25]
- filter: mlp
value: [0.25, 0.75]
- filter: layernorm
value: [0.5, 0.5]
- filter: modelnorm
value: [0.75]
- value: 0.5 # fallback for rest of tensors
dtype: float16
I use mergekit for all the manipulation told here.
This was named "Mistral-11B-TestBench11", keep that in mind while looking trough this.
hf-causal-experimental (pretrained=/content/drive/MyDrive/Mistral-11B-Test), limit: None, provide_description: False, num_fewshot: 0, batch_size: 4
| Task | Version | Metric | Value | Stderr | |
|---|---|---|---|---|---|
| arc_challenge | 0 | acc | 0.5597 | ± | 0.0145 |
| acc_norm | 0.5819 | ± | 0.0144 | ||
| arc_easy | 0 | acc | 0.8308 | ± | 0.0077 |
| acc_norm | 0.8215 | ± | 0.0079 | ||
| hellaswag | 0 | acc | 0.6371 | ± | 0.0048 |
| acc_norm | 0.8213 | ± | 0.0038 | ||
| piqa | 0 | acc | 0.8134 | ± | 0.0091 |
| acc_norm | 0.8275 | ± | 0.0088 | ||
| truthfulqa_mc | 1 | mc1 | 0.3990 | ± | 0.0171 |
| mc2 | 0.5685 | ± | 0.0155 | ||
| winogrande | 0 | acc | 0.7474 | ± | 0.0122 |

This model seem to be the best out of my 3 latest try:


You can find all the work I have done trying on this Pastebin.
Special thanks to Sushi, Henky for the machine he give me for big task, and Charles Goddard for his amazing tool.
If you want to support me, you can here.
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 53.01 |
| ARC (25-shot) | 64.42 |
| HellaSwag (10-shot) | 83.93 |
| MMLU (5-shot) | 63.82 |
| TruthfulQA (0-shot) | 56.68 |
| Winogrande (5-shot) | 77.74 |
| GSM8K (5-shot) | 14.94 |
| DROP (3-shot) | 9.57 |