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nbeerbower_-_mistral-nemo-wissenschaft-12B-gguf – AI Model by RichardErkhov | AlphaNeural AI
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mistral-nemo-wissenschaft-12B - GGUF
Model creator:
https://huggingface.co/nbeerbower/
Original model:
https://huggingface.co/nbeerbower/mistral-nemo-wissenschaft-12B/
Name
Quant method
Size
mistral-nemo-wissenschaft-12B.Q2_K.gguf
Q2_K
4.46GB
mistral-nemo-wissenschaft-12B.IQ3_XS.gguf
IQ3_XS
4.94GB
mistral-nemo-wissenschaft-12B.IQ3_S.gguf
IQ3_S
5.18GB
mistral-nemo-wissenschaft-12B.Q3_K_S.gguf
Q3_K_S
5.15GB
mistral-nemo-wissenschaft-12B.IQ3_M.gguf
IQ3_M
5.33GB
mistral-nemo-wissenschaft-12B.Q3_K.gguf
Q3_K
5.67GB
mistral-nemo-wissenschaft-12B.Q3_K_M.gguf
Q3_K_M
5.67GB
mistral-nemo-wissenschaft-12B.Q3_K_L.gguf
Q3_K_L
6.11GB
mistral-nemo-wissenschaft-12B.IQ4_XS.gguf
IQ4_XS
6.33GB
mistral-nemo-wissenschaft-12B.Q4_0.gguf
Q4_0
6.59GB
mistral-nemo-wissenschaft-12B.IQ4_NL.gguf
IQ4_NL
6.65GB
mistral-nemo-wissenschaft-12B.Q4_K_S.gguf
Q4_K_S
6.63GB
mistral-nemo-wissenschaft-12B.Q4_K.gguf
Q4_K
6.96GB
mistral-nemo-wissenschaft-12B.Q4_K_M.gguf
Q4_K_M
6.96GB
mistral-nemo-wissenschaft-12B.Q4_1.gguf
Q4_1
7.26GB
mistral-nemo-wissenschaft-12B.Q5_0.gguf
Q5_0
7.93GB
mistral-nemo-wissenschaft-12B.Q5_K_S.gguf
Q5_K_S
7.93GB
mistral-nemo-wissenschaft-12B.Q5_K.gguf
Q5_K
8.13GB
mistral-nemo-wissenschaft-12B.Q5_K_M.gguf
Q5_K_M
8.13GB
mistral-nemo-wissenschaft-12B.Q5_1.gguf
Q5_1
8.61GB
mistral-nemo-wissenschaft-12B.Q6_K.gguf
Q6_K
9.37GB
mistral-nemo-wissenschaft-12B.Q8_0.gguf
Q8_0
12.13GB
Original model description:
license: apache-2.0 library_name: transformers base_model:
mistralai/Mistral-Nemo-Instruct-2407 datasets:
tasksource/ScienceQA_text_only model-index:
name: mistral-nemo-wissenschaft-12B 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: 65.2 name: strict accuracy source: url:
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-wissenschaft-12B
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: 29.57 name: normalized accuracy source: url:
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-wissenschaft-12B
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: 6.57 name: exact match source: url:
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-wissenschaft-12B
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: 5.7 name: acc_norm source: url:
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-wissenschaft-12B
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: 12.29 name: acc_norm source: url:
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-wissenschaft-12B
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: 28.14 name: accuracy source: url:
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=nbeerbower/mistral-nemo-wissenschaft-12B
name: Open LLM Leaderboard
mistral-nemo-wissenschaft-12B
mistralai/Mistral-Nemo-Instruct-2407
finetuned on
tasksource/ScienceQA_text_only
.
Method
Finetuned using an A100 on Google Colab for 1 epoch. Correct answers were selected as the chosen answer, a random wrong answer was selected as "rejected."
Fine-tune Llama 3 with ORPO
Open LLM Leaderboard Evaluation Results
Detailed results can be found
here
Metric
Value
Avg.
24.58
IFEval (0-Shot)
65.20
BBH (3-Shot)
29.57
MATH Lvl 5 (4-Shot)
6.57
GPQA (0-shot)
5.70
MuSR (0-shot)
12.29
MMLU-PRO (5-shot)
28.14