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RichardErkhov/
kyujinpy_-_SOLAR-Platypus-10.7B-v1-gguf

Name: kyujinpy_-_SOLAR-Platypus-10.7B-v1-gguf
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SOLAR-Platypus-10.7B-v1 - GGUF

Model creator: https://huggingface.co/kyujinpy/
Original model: https://huggingface.co/kyujinpy/SOLAR-Platypus-10.7B-v1/

Name	Quant method	Size
SOLAR-Platypus-10.7B-v1.Q2_K.gguf	Q2_K	3.73GB
SOLAR-Platypus-10.7B-v1.IQ3_XS.gguf	IQ3_XS	4.14GB
SOLAR-Platypus-10.7B-v1.IQ3_S.gguf	IQ3_S	4.37GB
SOLAR-Platypus-10.7B-v1.Q3_K_S.gguf	Q3_K_S	4.34GB
SOLAR-Platypus-10.7B-v1.IQ3_M.gguf	IQ3_M	4.51GB
SOLAR-Platypus-10.7B-v1.Q3_K.gguf	Q3_K	4.84GB
SOLAR-Platypus-10.7B-v1.Q3_K_M.gguf	Q3_K_M	4.84GB
SOLAR-Platypus-10.7B-v1.Q3_K_L.gguf	Q3_K_L	5.26GB
SOLAR-Platypus-10.7B-v1.IQ4_XS.gguf	IQ4_XS	5.43GB
SOLAR-Platypus-10.7B-v1.Q4_0.gguf	Q4_0	5.66GB
SOLAR-Platypus-10.7B-v1.IQ4_NL.gguf	IQ4_NL	5.72GB
SOLAR-Platypus-10.7B-v1.Q4_K_S.gguf	Q4_K_S	5.7GB
SOLAR-Platypus-10.7B-v1.Q4_K.gguf	Q4_K	6.02GB
SOLAR-Platypus-10.7B-v1.Q4_K_M.gguf	Q4_K_M	6.02GB
SOLAR-Platypus-10.7B-v1.Q4_1.gguf	Q4_1	6.27GB
SOLAR-Platypus-10.7B-v1.Q5_0.gguf	Q5_0	6.89GB
SOLAR-Platypus-10.7B-v1.Q5_K_S.gguf	Q5_K_S	6.89GB
SOLAR-Platypus-10.7B-v1.Q5_K.gguf	Q5_K	7.08GB
SOLAR-Platypus-10.7B-v1.Q5_K_M.gguf	Q5_K_M	7.08GB
SOLAR-Platypus-10.7B-v1.Q5_1.gguf	Q5_1	7.51GB
SOLAR-Platypus-10.7B-v1.Q6_K.gguf	Q6_K	8.2GB
SOLAR-Platypus-10.7B-v1.Q8_0.gguf	Q8_0	10.62GB

Original model description:

language:

en license: cc-by-nc-sa-4.0 library_name: transformers datasets:
kyujinpy/Open-platypus-Commercial pipeline_tag: text-generation model-index:
name: SOLAR-Platypus-10.7B-v1 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: 61.69 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kyujinpy/SOLAR-Platypus-10.7B-v1 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: 84.23 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kyujinpy/SOLAR-Platypus-10.7B-v1 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: 60.37 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kyujinpy/SOLAR-Platypus-10.7B-v1 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: 51.58 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kyujinpy/SOLAR-Platypus-10.7B-v1 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: 82.79 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kyujinpy/SOLAR-Platypus-10.7B-v1 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: 11.07 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=kyujinpy/SOLAR-Platypus-10.7B-v1 name: Open LLM Leaderboard

SOLAR-Platypus-10.7B-v1

Model Details

Model Developers Kyujin Han (kyujinpy)

Input Models input text only.

Output Models generate text only.

Model Architecture
SOLAR-Platypus-10.7B-v1 is an auto-regressive language model based on the Llama2 architecture.

Base Model
upstage/SOLAR-10.7B-v1.0

Training Dataset
kyujinpy/Open-platypus-Commercial.

Notice

While training, I used LoRA.
The lora_r values is 16.

Q-LoRA config

LoRA_r: 16
LoRA_alpha: 16
LoRA_dropout: 0.05
LoRA_target_modules: [gate_proj, up_proj, down_proj]

Prompt

Alpaca template.

Model Benchmark

Open leaderboard

Follow up as link.

Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
SOLAR-Platypus-10.7B-v1	58.62	61.69	84.23	60.37	51.58	82.79	11.07
SOLAR-Platypus-10.7B-v2	55.25	59.39	83.57	59.93	43.15	81.45	4.02
upstage/SOLAR-10.7B-v1.0	66.04	61.95	84.60	65.48	45.04	83.66	55.50

Implementation Code

python

### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

repo = "kyujinpy/SOLAR-Platypus-10.7B-v1"
OpenOrca = AutoModelForCausalLM.from_pretrained(
        repo,
        return_dict=True,
        torch_dtype=torch.float16,
        device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	58.62
AI2 Reasoning Challenge (25-Shot)	61.69
HellaSwag (10-Shot)	84.23
MMLU (5-Shot)	60.37
TruthfulQA (0-shot)	51.58
Winogrande (5-shot)	82.79
GSM8k (5-shot)	11.07

RichardErkhov/kyujinpy_-_SOLAR-Platypus-10.7B-v1-gguf