DavidAU/L3-Stheno-Maid-Blackroot-Grand-HORROR-16B

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.

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