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uukuguy/Mistral-7B-OpenOrca-lora

sourceHugging Facellama2updated 3y agoView on Hugging Face
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Mistral-7b-OpenOrca-lora

This is a test.

This LoRA model is extracted from the efficient parameter fine-tuned model (Mistral-7B-OpenOra), and now it needs to be verified whether this LoRA model can achieve comparable performance with the original model.

The final goal is to create a toolkit that can simultaneously load multiple LoRA modules, and automatically switch to the appropriate combination of LoRA modules based on user queries to generate the best answer.

The lora merged model is here

The source code is here

Mistral-7B-OpenOrca

  • —LLM Evaluation ...

Local Test

ARC_acc_norm (25-shot)HellaSwag_acc_norm (10-shot)MMLU_acc (5-shot)TruthfulQA_mc2 (0-shot)GSM8K_acc (8-shot)Open LLM Score
Mistral-7B-OpenOrca718361.42454065.11
r=256688464.2846.9534165.81
r=64678464.2647.324165.65
r=16658362.8446.953864.45

Open LLM Leaderboard

ARC_acc_norm (25-shot)HellaSwag_acc_norm (10-shot)MMLU_acc (5-shot)TruthfulQA_mc2 (0-shot)Open LLM Score
Mistral-7B-SlimOrca62.5483.8662.7754.2365.85
Mistral-7B-OpenOrca64.0883.9962.2453.0565.84

lm-evaluation-harness

Open LLM Leaderboard

MetricMistral-7B-OpenOrcaMistral-7B-OpenOrca-loraMistral-7B-OpenOrca-lora-merged
ARC64.08
HellaSwag83.99
MMLU62.24
TruthfulQA53.05
Average65.84

HumanEval

MetricMistral-7B-OpenOrcaMistral-7B-OpenOrca-loraMistral-7B-OpenOrca-lora-merged
humaneval-python35.976

Training procedure

The following bitsandbytes quantization config was used during training:

  • —quant_method: bitsandbytes
  • —loadin8bit: False
  • —loadin4bit: True
  • —llmint8threshold: 6.0
  • —llmint8skip_modules: None
  • —llmint8enablefp32cpu_offload: False
  • —llmint8hasfp16weight: False
  • —bnb4bitquant_type: nf4
  • —bnb4bitusedoublequant: True
  • —bnb4bitcompute_dtype: bfloat16

Framework versions

  • —PEFT 0.5.0

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

MetricValue
Avg.50.72
ARC (25-shot)61.95
HellaSwag (10-shot)83.62
MMLU (5-shot)64.16
TruthfulQA (0-shot)42.74
Winogrande (5-shot)79.08
GSM8K (5-shot)17.29
DROP (3-shot)6.19