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RichardErkhov/
SJ-Donald_-_SOLAR-10.7B-slerp-gguf

Name: SJ-Donald_-_SOLAR-10.7B-slerp-gguf
Availability: InStock

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SOLAR-10.7B-slerp - GGUF

Model creator: https://huggingface.co/SJ-Donald/
Original model: https://huggingface.co/SJ-Donald/SOLAR-10.7B-slerp/

Name	Quant method	Size
SOLAR-10.7B-slerp.Q2_K.gguf	Q2_K	3.8GB
SOLAR-10.7B-slerp.IQ3_XS.gguf	IQ3_XS	4.22GB
SOLAR-10.7B-slerp.IQ3_S.gguf	IQ3_S	4.45GB
SOLAR-10.7B-slerp.Q3_K_S.gguf	Q3_K_S	4.42GB
SOLAR-10.7B-slerp.IQ3_M.gguf	IQ3_M	4.59GB
SOLAR-10.7B-slerp.Q3_K.gguf	Q3_K	4.92GB
SOLAR-10.7B-slerp.Q3_K_M.gguf	Q3_K_M	4.92GB
SOLAR-10.7B-slerp.Q3_K_L.gguf	Q3_K_L	5.34GB
SOLAR-10.7B-slerp.IQ4_XS.gguf	IQ4_XS	5.51GB
SOLAR-10.7B-slerp.Q4_0.gguf	Q4_0	5.74GB
SOLAR-10.7B-slerp.IQ4_NL.gguf	IQ4_NL	5.8GB
SOLAR-10.7B-slerp.Q4_K_S.gguf	Q4_K_S	5.78GB
SOLAR-10.7B-slerp.Q4_K.gguf	Q4_K	6.1GB
SOLAR-10.7B-slerp.Q4_K_M.gguf	Q4_K_M	6.1GB
SOLAR-10.7B-slerp.Q4_1.gguf	Q4_1	6.36GB
SOLAR-10.7B-slerp.Q5_0.gguf	Q5_0	6.98GB
SOLAR-10.7B-slerp.Q5_K_S.gguf	Q5_K_S	6.98GB
SOLAR-10.7B-slerp.Q5_K.gguf	Q5_K	7.17GB
SOLAR-10.7B-slerp.Q5_K_M.gguf	Q5_K_M	7.17GB
SOLAR-10.7B-slerp.Q5_1.gguf	Q5_1	7.6GB
SOLAR-10.7B-slerp.Q6_K.gguf	Q6_K	8.3GB
SOLAR-10.7B-slerp.Q8_0.gguf	Q8_0	10.75GB

Original model description:

language:

ko license: cc-by-nc-4.0 tags:
merge
lazymergekit
LDCC/LDCC-SOLAR-10.7B
upstage/SOLAR-10.7B-Instruct-v1.0 base_model:
LDCC/LDCC-SOLAR-10.7B
upstage/SOLAR-10.7B-Instruct-v1.0 model-index:
name: SOLAR-10.7B-slerp 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: 68.17 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SOLAR-10.7B-slerp 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: 86.91 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SOLAR-10.7B-slerp 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: 66.73 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SOLAR-10.7B-slerp 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: 67.42 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SOLAR-10.7B-slerp 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: 84.06 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SOLAR-10.7B-slerp 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: 62.17 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SOLAR-10.7B-slerp name: Open LLM Leaderboard

SOLAR-10.7B-slerp

SOLAR-10.7B-slerp is a merge of the following models using mergekit:

Github

https://github.com/sunjin7725/SOLAR-10.7b-slerp

Benchmark

Open-Ko-LLM-Leaderboard

Average	Ko-ARC	Ko-HellaSwag	Ko-MMLU	Ko-TruthfulQA	Ko-CommonGen V2
56.93	53.58	62.03	53.31	57.16	58.56

How to use

Python

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = 'SJ-Donald/SOLAR-10.7B-slerp'

tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo,
    return_dict=True,
    torch_dtype=torch.float16,
    device_map='auto'
)

🧩 Configuration

yaml

slices:
  - sources:
      - model: LDCC/LDCC-SOLAR-10.7B
        layer_range: [0, 48]
      - model: upstage/SOLAR-10.7B-Instruct-v1.0
        layer_range: [0, 48]
merge_method: slerp
base_model: upstage/SOLAR-10.7B-Instruct-v1.0
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
tokenizer_source: union
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	72.58
AI2 Reasoning Challenge (25-Shot)	68.17
HellaSwag (10-Shot)	86.91
MMLU (5-Shot)	66.73
TruthfulQA (0-shot)	67.42
Winogrande (5-shot)	84.06
GSM8k (5-shot)	62.17

RichardErkhov/SJ-Donald_-_SOLAR-10.7B-slerp-gguf

Original model description:

SOLAR-10.7B-slerp

Github

Benchmark

Open-Ko-LLM-Leaderboard

How to use

🧩 Configuration

Open LLM Leaderboard Evaluation Results

RichardErkhov/
SJ-Donald_-_SOLAR-10.7B-slerp-gguf