Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) 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](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.Q2_K.gguf) | Q2_K | 3.8GB | | [SJ-SOLAR-10.7b-DPO.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.IQ3_XS.gguf) | IQ3_XS | 4.22GB | | [SJ-SOLAR-10.7b-DPO.IQ3_S.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.IQ3_S.gguf) | IQ3_S | 4.45GB | | [SJ-SOLAR-10.7b-DPO.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.Q3_K_S.gguf) | Q3_K_S | 4.42GB | | [SJ-SOLAR-10.7b-DPO.IQ3_M.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.IQ3_M.gguf) | IQ3_M | 4.59GB | | [SJ-SOLAR-10.7b-DPO.Q3_K.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.Q3_K.gguf) | Q3_K | 4.92GB | | [SJ-SOLAR-10.7b-DPO.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.Q3_K_M.gguf) | Q3_K_M | 4.92GB | | [SJ-SOLAR-10.7b-DPO.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.Q3_K_L.gguf) | Q3_K_L | 5.34GB | | [SJ-SOLAR-10.7b-DPO.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.IQ4_XS.gguf) | IQ4_XS | 5.51GB | | [SJ-SOLAR-10.7b-DPO.Q4_0.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.Q4_0.gguf) | Q4_0 | 5.74GB | | [SJ-SOLAR-10.7b-DPO.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.IQ4_NL.gguf) | IQ4_NL | 5.8GB | | [SJ-SOLAR-10.7b-DPO.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.Q4_K_S.gguf) | Q4_K_S | 5.78GB | | [SJ-SOLAR-10.7b-DPO.Q4_K.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.Q4_K.gguf) | Q4_K | 6.1GB | | [SJ-SOLAR-10.7b-DPO.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.Q4_K_M.gguf) | Q4_K_M | 6.1GB | | [SJ-SOLAR-10.7b-DPO.Q4_1.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.Q4_1.gguf) | Q4_1 | 6.36GB | | [SJ-SOLAR-10.7b-DPO.Q5_0.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.Q5_0.gguf) | Q5_0 | 6.98GB | | [SJ-SOLAR-10.7b-DPO.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.Q5_K_S.gguf) | Q5_K_S | 6.98GB | | [SJ-SOLAR-10.7b-DPO.Q5_K.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.Q5_K.gguf) | Q5_K | 7.17GB | | [SJ-SOLAR-10.7b-DPO.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.Q5_K_M.gguf) | Q5_K_M | 7.17GB | | [SJ-SOLAR-10.7b-DPO.Q5_1.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.Q5_1.gguf) | Q5_1 | 7.6GB | | [SJ-SOLAR-10.7b-DPO.Q6_K.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/SJ-SOLAR-10.7b-DPO.Q6_K.gguf) | Q6_K | 8.3GB | | [SJ-SOLAR-10.7b-DPO.Q8_0.gguf](https://huggingface.co/RichardErkhov/SJ-Donald_-_SJ-SOLAR-10.7b-DPO-gguf/blob/main/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 * [SJ-Donald/SOLAR-10.7B-slerp](https://huggingface.co/SJ-Donald/SOLAR-10.7B-slerp) ## Datasets * [SJ-Donald/orca-dpo-pairs-ko](https://huggingface.co/datasets/SJ-Donald/orca-dpo-pairs-ko) ## 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 ```Python 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 ```Python template = """### System: {{system_content}} ### User: {{question}} ### Assistant: """ ``` ## GGUF Version You can use gguf model file here! -> [SJ-Donald/SJ-SOLAR-10.7b-DPO-GGUF](https://huggingface.co/SJ-Donald/SJ-SOLAR-10.7b-DPO-GGUF) # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_SJ-Donald__SJ-SOLAR-10.7b-DPO) | 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|