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nlpguy_-_ColorShadow-7B-gguf
RichardErkhov/nlpguy_-_ColorShadow-7B-gguf
nlpguy_-_ColorShadow-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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.gguf95.2 GB · 100%
At a glance
- Access
- Public
- Created
- Jul 27, 2024
- Updated
- Jul 27, 2024
- SHA
- da4d8213
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
ColorShadow-7B - GGUF
- Model creator: https://huggingface.co/nlpguy/
- Original model: https://huggingface.co/nlpguy/ColorShadow-7B/
| Name | Quant method | Size |
|---|---|---|
| ColorShadow-7B.Q2_K.gguf | Q2_K | 2.53GB |
| ColorShadow-7B.IQ3_XS.gguf | IQ3_XS | 2.81GB |
| ColorShadow-7B.IQ3_S.gguf | IQ3_S | 2.96GB |
| ColorShadow-7B.Q3_K_S.gguf | Q3_K_S | 2.95GB |
| ColorShadow-7B.IQ3_M.gguf | IQ3_M | 3.06GB |
| ColorShadow-7B.Q3_K.gguf | Q3_K | 3.28GB |
| ColorShadow-7B.Q3_K_M.gguf | Q3_K_M | 3.28GB |
| ColorShadow-7B.Q3_K_L.gguf | Q3_K_L | 3.56GB |
| ColorShadow-7B.IQ4_XS.gguf | IQ4_XS | 3.67GB |
| ColorShadow-7B.Q4_0.gguf | Q4_0 | 3.83GB |
| ColorShadow-7B.IQ4_NL.gguf | IQ4_NL | 3.87GB |
| ColorShadow-7B.Q4_K_S.gguf | Q4_K_S | 3.86GB |
| ColorShadow-7B.Q4_K.gguf | Q4_K | 4.07GB |
| ColorShadow-7B.Q4_K_M.gguf | Q4_K_M | 4.07GB |
| ColorShadow-7B.Q4_1.gguf | Q4_1 | 4.24GB |
| ColorShadow-7B.Q5_0.gguf | Q5_0 | 4.65GB |
| ColorShadow-7B.Q5_K_S.gguf | Q5_K_S | 4.65GB |
| ColorShadow-7B.Q5_K.gguf | Q5_K | 4.78GB |
| ColorShadow-7B.Q5_K_M.gguf | Q5_K_M | 4.78GB |
| ColorShadow-7B.Q5_1.gguf | Q5_1 | 5.07GB |
| ColorShadow-7B.Q6_K.gguf | Q6_K | 5.53GB |
| ColorShadow-7B.Q8_0.gguf | Q8_0 | 7.17GB |
Original model description:
license: apache-2.0 tags:
- merge model-index:
- name: ColorShadow-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: 67.83 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/ColorShadow-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: 85.15 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/ColorShadow-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: 61.69 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/ColorShadow-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: 59.56 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/ColorShadow-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: 80.58 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/ColorShadow-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: 55.19 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nlpguy/ColorShadow-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:
ColorShadow-7B
This is a Gradient-SLERP merge between diffnamehard/Mistral-CatMacaroni-slerp-7B and cookinai/Valkyrie-V1 performed using mergekit.
Here is the config file used:
slices:
- sources:
- model: diffnamehard/Mistral-CatMacaroni-slerp-7B
layer_range: [0, 32]
- model: cookinai/Valkyrie-V1
layer_range: [0, 32]
merge_method: slerp
base_model: diffnamehard/Mistral-CatMacaroni-slerp-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5 # fallback for rest of tensors
dtype: float16
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 68.34 |
| AI2 Reasoning Challenge (25-Shot) | 67.83 |
| HellaSwag (10-Shot) | 85.15 |
| MMLU (5-Shot) | 61.69 |
| TruthfulQA (0-shot) | 59.56 |
| Winogrande (5-shot) | 80.58 |
| GSM8k (5-shot) | 55.19 |
- Created
- Jul 27, 2024
- Updated
- Jul 27, 2024