Fine-tune of Upstage AI's SOLAR-10.7B-Instruct-v1.0 model, using the OpenHermes, Platypus, and Capybara datasets. Additionally fine-tuned on Jon Durbin's Bagel v0.3, plus a few unreleased datasets.
Fine-tuned on 8x4090s for 1.25 epochs.
Model Sources [optional]
- Repository: TBD
- Demo: TBD
Bias, Risks, and Limitations
This fine-tune has had zero alignment, safety data, or anything else shoved down it's throat.
Training Details
Training Data
See the sidebar for links to the relevant datasets.
Training Procedure
Trained using QLORA via the Axolotl tool.
Evaluation
TBD
Training procedure
The following bitsandbytes
quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
Framework versions
- PEFT 0.6.0
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 70.94 |
AI2 Reasoning Challenge (25-Shot) | 69.03 |
HellaSwag (10-Shot) | 87.54 |
MMLU (5-Shot) | 66.19 |
TruthfulQA (0-shot) | 59.17 |
Winogrande (5-shot) | 83.19 |
GSM8k (5-shot) | 60.50 |
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Model tree for decapoda-research/Antares-11b-v2
Dataset used to train decapoda-research/Antares-11b-v2
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard69.030
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard87.540
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard66.190
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard59.170
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard83.190
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard60.500