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
Extract lora model Mistral-7B-OpenOrca-lora from Mistral-7B-OpenOrca;
Merge the base model Mistral-7B-v0.1 with lora model to Mistral-7B-OpenOrca-lora-merged
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-OpenOrca | 71 | 83 | 61.42 | 45 | 40 | 65.11 |
| r=256 | 68 | 84 | 64.28 | 46.953 | 41 | 65.81 |
| r=64 | 67 | 84 | 64.26 | 47.32 | 41 | 65.65 |
| r=16 | 65 | 83 | 62.84 | 46.95 | 38 | 64.45 |
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-SlimOrca | 62.54 | 83.86 | 62.77 | 54.23 | 65.85 |
| Mistral-7B-OpenOrca | 64.08 | 83.99 | 62.24 | 53.05 | 65.84 |
lm-evaluation-harness
| Metric | Mistral-7B-OpenOrca | Mistral-7B-OpenOrca-lora | Mistral-7B-OpenOrca-lora-merged |
|---|---|---|---|
| ARC | 64.08 | ||
| HellaSwag | 83.99 | ||
| MMLU | 62.24 | ||
| TruthfulQA | 53.05 | ||
| Average | 65.84 |
HumanEval
| Metric | Mistral-7B-OpenOrca | Mistral-7B-OpenOrca-lora | Mistral-7B-OpenOrca-lora-merged |
|---|---|---|---|
| humaneval-python | 35.976 |
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 0.5.0
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| 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 |
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