Grand Horror 16B
This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats. The source code can also be used directly.
IMPORTANT: Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers
If you are going to use this model, (source, GGUF or a different quant), please review this document for critical parameter, sampler and advance sampler settings (for multiple AI/LLM aps).
This a "Class 4" (default settings AND sampler settings are critical to operation) model:
For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) (especially for use case(s) beyond the model's design) please see:
REASON:
Regardless of "model class" this document will detail methods to enhance operations.
If the model is a Class 3/4 model the default settings (parameters, samplers, advanced samplers) must be set for "use case(s)" uses correctly. Some AI/LLM apps DO NOT have consistant default setting(s) which result in sub-par model operation. Like wise for Class 3/4 models (which operate somewhat to very differently than standard models) additional samplers and advanced samplers settings are required to "smooth out" operation, AND/OR also allow full operation for use cases the model was not designed for.
BONUS - Use these settings for ANY model, ANY repo, ANY quant (including source/full precision):
This document also details parameters, sampler and advanced samplers that can be use FOR ANY MODEL, FROM ANY REPO too - all quants, and of course source code operation too - to enhance the operation of any model.
NOTE:
I strongly suggest you also visit the DavidAU GGUF (below) repo too for more details in using this model ; especially if it is "Class 3" or "Class 4" to get maximum performance from the model.
For full information about this model, including:
- Details about this model and its use case(s).
- Context limits
- Special usage notes / settings.
- Any model(s) used to create this model.
- Template(s) used to access/use this model.
- Example generation(s)
- GGUF quants of this model
Please go to:
[ https://huggingface.co/DavidAU/L3-Stheno-Maid-Blackroot-Grand-HORROR-16B-GGUF ]
and IMATRIX GGUFS:
[ https://huggingface.co/DavidAU/L3-Stheno-Maid-Blackroot-Grand-HORROR-16B-Ultra-NEO-V2-IMATRIX-GGUF ]
Additional Quants:
Imatrix GGUF:
[ https://huggingface.co/mradermacher/L3-Stheno-Maid-Blackroot-Grand-HORROR-16B-i1-GGUF ]
GGUF:
[ https://huggingface.co/mradermacher/L3-Stheno-Maid-Blackroot-Grand-HORROR-16B-GGUF ]
GGUF:
[ https://huggingface.co/backyardai/L3-Stheno-Maid-Blackroot-Grand-HORROR-16B-GGUF ]
SOURCE:
[ https://huggingface.co/backyardai/L3-Stheno-Maid-Blackroot-Grand-HORROR-16B ]
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the passthrough merge method.
Models Merged
The following models were included in the merge:
- G:/7B/Llama-3-Lumimaid-8B-v0.1-OAS
- G:/7B/L3-8B-Stheno-v3.2
- G:/7B/Jamet-8B-L3-MK.V-Blackroot
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: G:/7B/L3-8B-Stheno-v3.2
layer_range: [0, 14]
- sources:
- model: G:/7B/Llama-3-Lumimaid-8B-v0.1-OAS
layer_range: [8, 20]
- sources:
- model: G:/7B/Jamet-8B-L3-MK.V-Blackroot
layer_range: [12, 24]
- sources:
- model: G:/7B/L3-8B-Stheno-v3.2
layer_range: [14, 28]
- sources:
- model: G:/7B/Llama-3-Lumimaid-8B-v0.1-OAS
layer_range: [20, 31]
- sources:
- model: G:/7B/Jamet-8B-L3-MK.V-Blackroot
layer_range: [24, 32]
merge_method: passthrough
dtype: float16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 17.10 |
IFEval (0-Shot) | 34.39 |
BBH (3-Shot) | 26.69 |
MATH Lvl 5 (4-Shot) | 1.59 |
GPQA (0-shot) | 2.80 |
MuSR (0-shot) | 8.56 |
MMLU-PRO (5-shot) | 28.56 |
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard34.390
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard26.690
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard1.590
- acc_norm on GPQA (0-shot)Open LLM Leaderboard2.800
- acc_norm on MuSR (0-shot)Open LLM Leaderboard8.560
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard28.560