metadata
base_model:
- mistralai/Mistral-7B-v0.1
- berkeley-nest/Starling-LM-7B-alpha
- mlabonne/AlphaMonarch-7B
- cognitivecomputations/WestLake-7B-v2-laser
- senseable/garten2-7b
library_name: transformers
tags:
- mergekit
- merge
license: cc-by-nc-4.0
model-index:
- name: Starling_Monarch_Westlake_Garten-7B-v0.1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: EQ-Bench
type: eq-bench
config: EQ-Bench
split: v2.1
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 80.01
name: self-reported
source:
url: https://github.com/EQ-bench/EQ-Bench
name: EQ-Bench v2.1
- 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: 71.76
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1
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: 88.15
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1
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: 65.07
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1
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: 67.92
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1
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: 82.16
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1
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: 71.95
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1
name: Open LLM Leaderboard
quantized_by: bartowski
pipeline_tag: text-generation
Llamacpp Quantizations of Starling_Monarch_Westlake_Garten-7B-v0.1
Using llama.cpp release b2440 for quantization.
Original model: https://huggingface.co/giraffe176/Starling_Monarch_Westlake_Garten-7B-v0.1
Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| Starling_Monarch_Westlake_Garten-7B-v0.1-Q8_0.gguf | Q8_0 | 7.69GB | Extremely high quality, generally unneeded but max available quant. |
| Starling_Monarch_Westlake_Garten-7B-v0.1-Q6_K.gguf | Q6_K | 5.94GB | Very high quality, near perfect, recommended. |
| Starling_Monarch_Westlake_Garten-7B-v0.1-Q5_K_M.gguf | Q5_K_M | 5.13GB | High quality, very usable. |
| Starling_Monarch_Westlake_Garten-7B-v0.1-Q5_K_S.gguf | Q5_K_S | 4.99GB | High quality, very usable. |
| Starling_Monarch_Westlake_Garten-7B-v0.1-Q5_0.gguf | Q5_0 | 4.99GB | High quality, older format, generally not recommended. |
| Starling_Monarch_Westlake_Garten-7B-v0.1-Q4_K_M.gguf | Q4_K_M | 4.36GB | Good quality, similar to 4.25 bpw. |
| Starling_Monarch_Westlake_Garten-7B-v0.1-Q4_K_S.gguf | Q4_K_S | 4.14GB | Slightly lower quality with small space savings. |
| Starling_Monarch_Westlake_Garten-7B-v0.1-IQ4_NL.gguf | IQ4_NL | 4.15GB | Good quality, similar to Q4_K_S, new method of quanting, |
| Starling_Monarch_Westlake_Garten-7B-v0.1-IQ4_XS.gguf | IQ4_XS | 3.94GB | Decent quality, new method with similar performance to Q4. |
| Starling_Monarch_Westlake_Garten-7B-v0.1-Q4_0.gguf | Q4_0 | 4.10GB | Decent quality, older format, generally not recommended. |
| Starling_Monarch_Westlake_Garten-7B-v0.1-IQ3_M.gguf | IQ3_M | 3.28GB | Medium-low quality, new method with decent performance. |
| Starling_Monarch_Westlake_Garten-7B-v0.1-IQ3_S.gguf | IQ3_S | 3.18GB | Lower quality, new method with decent performance, recommended over Q3 quants. |
| Starling_Monarch_Westlake_Garten-7B-v0.1-Q3_K_L.gguf | Q3_K_L | 3.82GB | Lower quality but usable, good for low RAM availability. |
| Starling_Monarch_Westlake_Garten-7B-v0.1-Q3_K_M.gguf | Q3_K_M | 3.51GB | Even lower quality. |
| Starling_Monarch_Westlake_Garten-7B-v0.1-Q3_K_S.gguf | Q3_K_S | 3.16GB | Low quality, not recommended. |
| Starling_Monarch_Westlake_Garten-7B-v0.1-Q2_K.gguf | Q2_K | 2.71GB | Extremely low quality, not recommended. |
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