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Nitral-AI_-_KukulStanta-7B-gguf
RichardErkhov/Nitral-AI_-_KukulStanta-7B-gguf
Nitral-AI_-_KukulStanta-7B-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
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At a glance
- Access
- Public
- Created
- Sep 16, 2024
- Updated
- Sep 16, 2024
- SHA
- 6ab78077
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
KukulStanta-7B - GGUF
- Model creator: https://huggingface.co/Nitral-AI/
- Original model: https://huggingface.co/Nitral-AI/KukulStanta-7B/
| Name | Quant method | Size |
|---|---|---|
| KukulStanta-7B.Q2_K.gguf | Q2_K | 2.53GB |
| KukulStanta-7B.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| KukulStanta-7B.IQ3_S.gguf | IQ3_S | 2.96GB |
| KukulStanta-7B.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| KukulStanta-7B.IQ3_M.gguf | IQ3_M | 3.06GB |
| KukulStanta-7B.Q3_K.gguf | Q3_K | 3.28GB |
| KukulStanta-7B.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| KukulStanta-7B.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| KukulStanta-7B.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| KukulStanta-7B.Q4_0.gguf | Q4_0 | 3.83GB |
| KukulStanta-7B.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| KukulStanta-7B.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| KukulStanta-7B.Q4_K.gguf | Q4_K | 4.07GB |
| KukulStanta-7B.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| KukulStanta-7B.Q4_1.gguf | Q4_1 | 4.24GB |
| KukulStanta-7B.Q5_0.gguf | Q5_0 | 4.65GB |
| KukulStanta-7B.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| KukulStanta-7B.Q5_K.gguf | Q5_K | 4.78GB |
| KukulStanta-7B.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| KukulStanta-7B.Q5_1.gguf | Q5_1 | 5.07GB |
| KukulStanta-7B.Q6_K.gguf | Q6_K | 5.53GB |
| KukulStanta-7B.Q8_0.gguf | Q8_0 | 7.17GB |
Original model description:
license: other model-index:
- name: KukulStanta-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm value: 68.43 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Nitral-AI/KukulStanta-7B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm value: 86.37 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Nitral-AI/KukulStanta-7B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc value: 65.0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Nitral-AI/KukulStanta-7B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2 value: 62.19 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Nitral-AI/KukulStanta-7B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc value: 80.03 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Nitral-AI/KukulStanta-7B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc value: 63.68 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Nitral-AI/KukulStanta-7B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
Vision/multimodal capabilities:
Quants Found Here: https://huggingface.co/Lewdiculous/KukulStanta-7B-GGUF-IQ-Imatrix
If you want to use vision functionality:
- You must use the latest versions of Koboldcpp.
To use the multimodal capabilities of this model and use vision you need to load the specified mmproj file, this can be found inside this model repo.
- You can load the mmproj by using the corresponding section in the interface:
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 70.95 |
| AI2 Reasoning Challenge (25-Shot) | 68.43 |
| HellaSwag (10-Shot) | 86.37 |
| MMLU (5-Shot) | 65.00 |
| TruthfulQA (0-shot) | 62.19 |
| Winogrande (5-shot) | 80.03 |
| GSM8k (5-shot) | 63.68 |
- Created
- Sep 16, 2024
- Updated
- Sep 16, 2024