Quantization made by Richard Erkhov.
SJ-SOLAR-10.7b-DPO - GGUF
- Model creator: https://huggingface.co/SJ-Donald/
- Original model: https://huggingface.co/SJ-Donald/SJ-SOLAR-10.7b-DPO/
Name | Quant method | Size |
---|---|---|
SJ-SOLAR-10.7b-DPO.Q2_K.gguf | Q2_K | 3.8GB |
SJ-SOLAR-10.7b-DPO.IQ3_XS.gguf | IQ3_XS | 4.22GB |
SJ-SOLAR-10.7b-DPO.IQ3_S.gguf | IQ3_S | 4.45GB |
SJ-SOLAR-10.7b-DPO.Q3_K_S.gguf | Q3_K_S | 4.42GB |
SJ-SOLAR-10.7b-DPO.IQ3_M.gguf | IQ3_M | 4.59GB |
SJ-SOLAR-10.7b-DPO.Q3_K.gguf | Q3_K | 4.92GB |
SJ-SOLAR-10.7b-DPO.Q3_K_M.gguf | Q3_K_M | 4.92GB |
SJ-SOLAR-10.7b-DPO.Q3_K_L.gguf | Q3_K_L | 5.34GB |
SJ-SOLAR-10.7b-DPO.IQ4_XS.gguf | IQ4_XS | 5.51GB |
SJ-SOLAR-10.7b-DPO.Q4_0.gguf | Q4_0 | 5.74GB |
SJ-SOLAR-10.7b-DPO.IQ4_NL.gguf | IQ4_NL | 5.8GB |
SJ-SOLAR-10.7b-DPO.Q4_K_S.gguf | Q4_K_S | 5.78GB |
SJ-SOLAR-10.7b-DPO.Q4_K.gguf | Q4_K | 6.1GB |
SJ-SOLAR-10.7b-DPO.Q4_K_M.gguf | Q4_K_M | 6.1GB |
SJ-SOLAR-10.7b-DPO.Q4_1.gguf | Q4_1 | 6.36GB |
SJ-SOLAR-10.7b-DPO.Q5_0.gguf | Q5_0 | 6.98GB |
SJ-SOLAR-10.7b-DPO.Q5_K_S.gguf | Q5_K_S | 6.98GB |
SJ-SOLAR-10.7b-DPO.Q5_K.gguf | Q5_K | 7.17GB |
SJ-SOLAR-10.7b-DPO.Q5_K_M.gguf | Q5_K_M | 7.17GB |
SJ-SOLAR-10.7b-DPO.Q5_1.gguf | Q5_1 | 7.6GB |
SJ-SOLAR-10.7b-DPO.Q6_K.gguf | Q6_K | 8.3GB |
SJ-SOLAR-10.7b-DPO.Q8_0.gguf | Q8_0 | 10.75GB |
Original model description:
license: cc-by-nc-4.0 tags: - DPO model-index: - name: SJ-SOLAR-10.7b-DPO 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: 68.26 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SJ-SOLAR-10.7b-DPO 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: 86.95 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SJ-SOLAR-10.7b-DPO 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/SJ-SOLAR-10.7b-DPO 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: 67.74 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SJ-SOLAR-10.7b-DPO 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: 84.21 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SJ-SOLAR-10.7b-DPO 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: 62.09 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=SJ-Donald/SJ-SOLAR-10.7b-DPO name: Open LLM Leaderboard
SJ-Donald/SJ-SOLAR-10.7b-DPO
SJ-Donald/SJ-SOLAR-10.7b-DPO is fine-tuned using DPO method.
Environment
Using Google CoLab A100
Base model
Datasets
Benchmark
Open-LLM-Leaderboard(https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
---|---|---|---|---|---|---|
72.67 | 68.26 | 86.95 | 66.73 | 67.74 | 84.21 | 62.03 |
open-ko-llm-leaderboard(https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)
Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
---|---|---|---|---|---|
56.93 | 53.67 | 61.99 | 53.36 | 57.2 | 58.44 |
How to use
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
repo = 'SJ-Donald/SJ-SOLAR-10.7b-DPO'
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
Chat Template
template = """### System:
{{system_content}}
### User:
{{question}}
### Assistant:
"""
GGUF Version
You can use gguf model file here! -> SJ-Donald/SJ-SOLAR-10.7b-DPO-GGUF
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 72.67 |
AI2 Reasoning Challenge (25-Shot) | 68.26 |
HellaSwag (10-Shot) | 86.95 |
MMLU (5-Shot) | 66.73 |
TruthfulQA (0-shot) | 67.74 |
Winogrande (5-shot) | 84.21 |
GSM8k (5-shot) | 62.09 |