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PracticeLLM_-_SOLAR-tail-10.7B-instruct-v1.0-gguf
RichardErkhov/PracticeLLM_-_SOLAR-tail-10.7B-instruct-v1.0-gguf
PracticeLLM_-_SOLAR-tail-10.7B-instruct-v1.0-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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At a glance
- Access
- Public
- Created
- Jul 22, 2024
- Updated
- Jul 23, 2024
- SHA
- a4365700
Model card
From the Hugging Face model README
Quantization made by Richard Erkhov.
SOLAR-tail-10.7B-instruct-v1.0 - GGUF
- Model creator: https://huggingface.co/PracticeLLM/
- Original model: https://huggingface.co/PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0/
Original model description:
language:
- en
- ko datasets:
- kyujinpy/KOR-OpenOrca-Platypus-v3 pipeline_tag: text-generation license: cc-by-nc-sa-4.0
SOLAR-tail-10.7B-instruct-v1.0
Model Details
Model Developers Kyujin Han (kyujinpy)
Method
Instruction-tuning with PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0.
Datasets
datasets: kyujinpy/KOR-OpenOrca-Platypus-v3.
Hyperparameters
python finetune.py \
--base_model PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 \
--data-path kyujinpy/KOR-OpenOrca-Platypus-v3 \
--output_dir ./SOLAR-tail-10.7B-instruct \
--batch_size 64 \
--micro_batch_size 1 \
--num_epochs 1 \
--learning_rate 3e-5 \
--cutoff_len 4096 \
--val_set_size 0 \
--lora_r 16 \
--lora_alpha 16 \
--lora_dropout 0.05 \
--lora_target_modules '[q_proj, k_proj, v_proj, o_proj, gate_proj, down_proj, up_proj, lm_head]' \
--train_on_inputs False \
--add_eos_token False \
--group_by_length False \
--prompt_template_name user_prompt \
--lr_scheduler 'cosine' \
Platypus repo.
Model Benchmark
Open leaderboard
- Follow up as link.
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Ko-CommonGenV2 |
|---|---|---|---|---|---|---|
| PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0 | 51.70 | 46.93 | 58.19 | 53.15 | 46.52 | 53.72 |
| PracticeLLM/SOLAR-tail-10.7B-Merge-v1.0 | 48.32 | 45.73 | 56.97 | 38.77 | 38.75 | 61.16 |
| jjourney1125/M-SOLAR-10.7B-v1.0 | 55.15 | 49.57 | 60.12 | 54.60 | 49.23 | 62.22 |
Implementation Code
### KO-Platypus
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "PracticeLLM/SOLAR-tail-10.7B-instruct-v1.0"
OpenOrca = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
OpenOrca_tokenizer = AutoTokenizer.from_pretrained(repo)
- Created
- Jul 22, 2024
- Updated
- Jul 23, 2024