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RichardErkhov/
GalrionSoftworks_-_MagnusIntellectus-12B-v1-gguf

Name: GalrionSoftworks_-_MagnusIntellectus-12B-v1-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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MagnusIntellectus-12B-v1 - GGUF

Model creator: https://huggingface.co/GalrionSoftworks/
Original model: https://huggingface.co/GalrionSoftworks/MagnusIntellectus-12B-v1/

Name	Quant method	Size
MagnusIntellectus-12B-v1.Q2_K.gguf	Q2_K	4.46GB
MagnusIntellectus-12B-v1.IQ3_XS.gguf	IQ3_XS	4.94GB
MagnusIntellectus-12B-v1.IQ3_S.gguf	IQ3_S	5.18GB
MagnusIntellectus-12B-v1.Q3_K_S.gguf	Q3_K_S	5.15GB
MagnusIntellectus-12B-v1.IQ3_M.gguf	IQ3_M	5.33GB
MagnusIntellectus-12B-v1.Q3_K.gguf	Q3_K	5.67GB
MagnusIntellectus-12B-v1.Q3_K_M.gguf	Q3_K_M	5.67GB
MagnusIntellectus-12B-v1.Q3_K_L.gguf	Q3_K_L	6.11GB
MagnusIntellectus-12B-v1.IQ4_XS.gguf	IQ4_XS	6.33GB
MagnusIntellectus-12B-v1.Q4_0.gguf	Q4_0	6.59GB
MagnusIntellectus-12B-v1.IQ4_NL.gguf	IQ4_NL	6.65GB
MagnusIntellectus-12B-v1.Q4_K_S.gguf	Q4_K_S	6.63GB
MagnusIntellectus-12B-v1.Q4_K.gguf	Q4_K	6.96GB
MagnusIntellectus-12B-v1.Q4_K_M.gguf	Q4_K_M	6.96GB
MagnusIntellectus-12B-v1.Q4_1.gguf	Q4_1	7.26GB
MagnusIntellectus-12B-v1.Q5_0.gguf	Q5_0	7.93GB
MagnusIntellectus-12B-v1.Q5_K_S.gguf	Q5_K_S	7.93GB
MagnusIntellectus-12B-v1.Q5_K.gguf	Q5_K	8.13GB
MagnusIntellectus-12B-v1.Q5_K_M.gguf	Q5_K_M	8.13GB
MagnusIntellectus-12B-v1.Q5_1.gguf	Q5_1	8.61GB
MagnusIntellectus-12B-v1.Q6_K.gguf	Q6_K	9.37GB
MagnusIntellectus-12B-v1.Q8_0.gguf	Q8_0	12.13GB

Original model description:

tags:

merge
mergekit
lazymergekit
UsernameJustAnother/Nemo-12B-Marlin-v5
anthracite-org/magnum-12b-v2 base_model:
UsernameJustAnother/Nemo-12B-Marlin-v5
anthracite-org/magnum-12b-v2 model-index:
name: MagnusIntellectus-12B-v1 results:
- task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics:
  - type: inst_level_strict_acc and prompt_level_strict_acc value: 44.21 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MagnusIntellectus-12B-v1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics:
  - type: acc_norm value: 33.26 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MagnusIntellectus-12B-v1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics:
  - type: exact_match value: 5.14 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MagnusIntellectus-12B-v1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics:
  - type: acc_norm value: 4.59 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MagnusIntellectus-12B-v1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics:
  - type: acc_norm value: 15.18 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MagnusIntellectus-12B-v1 name: Open LLM Leaderboard
- task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics:
  - type: acc value: 26.9 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=GalrionSoftworks/MagnusIntellectus-12B-v1 name: Open LLM Leaderboard license: apache-2.0 pipeline_tag: text-generation library_name: transformers

MagnusIntellectus-12B-v1

How pleasant, the rocks appear to have made a decent conglomerate. A-.

MagnusIntellectus is a merge of the following models using LazyMergekit:

🧩 Configuration

yaml

models:
  - model: UsernameJustAnother/Nemo-12B-Marlin-v5
    parameters:
      density: 0.4
      weight: 0.70
  - model: anthracite-org/magnum-12b-v2
    parameters:
      density: 0.6
      weight: 0.30

merge_method: ties
base_model: UsernameJustAnother/Nemo-12B-Marlin-v5
parameters:
  normalize: true
dtype: bfloat16

💻 Usage

python

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "GalrionSoftworks/MagnusIntellectus-12B-v1"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	21.55
IFEval (0-Shot)	44.21
BBH (3-Shot)	33.26
MATH Lvl 5 (4-Shot)	5.14
GPQA (0-shot)	4.59
MuSR (0-shot)	15.18
MMLU-PRO (5-shot)	26.90

Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more quants, at much higher speed, than I would otherwise be able to.

RichardErkhov/GalrionSoftworks_-_MagnusIntellectus-12B-v1-gguf