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nbeerbower_-_Gemma2-Gutenberg-Doppel-9B-gguf – AI Model by RichardErkhov | AlphaNeural AI
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Gemma2-Gutenberg-Doppel-9B - GGUF
Model creator:
https://huggingface.co/nbeerbower/
Original model:
https://huggingface.co/nbeerbower/Gemma2-Gutenberg-Doppel-9B/
Name
Quant method
Size
Gemma2-Gutenberg-Doppel-9B.Q2_K.gguf
Q2_K
3.54GB
Gemma2-Gutenberg-Doppel-9B.IQ3_XS.gguf
IQ3_XS
3.86GB
Gemma2-Gutenberg-Doppel-9B.IQ3_S.gguf
IQ3_S
4.04GB
Gemma2-Gutenberg-Doppel-9B.Q3_K_S.gguf
Q3_K_S
4.04GB
Gemma2-Gutenberg-Doppel-9B.IQ3_M.gguf
IQ3_M
4.19GB
Gemma2-Gutenberg-Doppel-9B.Q3_K.gguf
Q3_K
4.43GB
Gemma2-Gutenberg-Doppel-9B.Q3_K_M.gguf
Q3_K_M
4.43GB
Gemma2-Gutenberg-Doppel-9B.Q3_K_L.gguf
Q3_K_L
4.78GB
Gemma2-Gutenberg-Doppel-9B.IQ4_XS.gguf
IQ4_XS
4.86GB
Gemma2-Gutenberg-Doppel-9B.Q4_0.gguf
Q4_0
5.07GB
Gemma2-Gutenberg-Doppel-9B.IQ4_NL.gguf
IQ4_NL
5.1GB
Gemma2-Gutenberg-Doppel-9B.Q4_K_S.gguf
Q4_K_S
5.1GB
Gemma2-Gutenberg-Doppel-9B.Q4_K.gguf
Q4_K
5.37GB
Gemma2-Gutenberg-Doppel-9B.Q4_K_M.gguf
Q4_K_M
5.37GB
Gemma2-Gutenberg-Doppel-9B.Q4_1.gguf
Q4_1
5.55GB
Gemma2-Gutenberg-Doppel-9B.Q5_0.gguf
Q5_0
6.04GB
Gemma2-Gutenberg-Doppel-9B.Q5_K_S.gguf
Q5_K_S
6.04GB
Gemma2-Gutenberg-Doppel-9B.Q5_K.gguf
Q5_K
6.19GB
Gemma2-Gutenberg-Doppel-9B.Q5_K_M.gguf
Q5_K_M
6.19GB
Gemma2-Gutenberg-Doppel-9B.Q5_1.gguf
Q5_1
6.52GB
Gemma2-Gutenberg-Doppel-9B.Q6_K.gguf
Q6_K
7.07GB
Gemma2-Gutenberg-Doppel-9B.Q8_0.gguf
Q8_0
9.15GB
Original model description:
license: gemma library_name: transformers base_model:
UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3 datasets:
jondurbin/gutenberg-dpo-v0.1
nbeerbower/gutenberg2-dpo model-index:
name: Gemma2-Gutenberg-Doppel-9B 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: 71.71 name: strict accuracy source: url:
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Gemma2-Gutenberg-Doppel-9B
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: 41.08 name: normalized accuracy source: url:
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Gemma2-Gutenberg-Doppel-9B
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: 3.47 name: exact match source: url:
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Gemma2-Gutenberg-Doppel-9B
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: 10.63 name: acc_norm source: url:
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Gemma2-Gutenberg-Doppel-9B
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: 17.3 name: acc_norm source: url:
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Gemma2-Gutenberg-Doppel-9B
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: 34.75 name: accuracy source: url:
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/Gemma2-Gutenberg-Doppel-9B
name: Open LLM Leaderboard
image/png
Gemma2-Gutenberg-Doppel-9B
UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
finetuned on
jondurbin/gutenberg-dpo-v0.1
and
nbeerbower/gutenberg2-dpo
.
Method
ORPO finetuned
using 2x A40 for 3 epochs.
Open LLM Leaderboard Evaluation Results
Detailed results can be found
here
Metric
Value
Avg.
29.82
IFEval (0-Shot)
71.71
BBH (3-Shot)
41.08
MATH Lvl 5 (4-Shot)
3.47
GPQA (0-shot)
10.63
MuSR (0-shot)
17.30
MMLU-PRO (5-shot)
34.75