---
language:
- en
library_name: peft
pipeline_tag: text-generation
tags:
- Mistral
license: llama2
model-index:
- name: SpeechlessCoder
  results:
  - task:
      type: text-generation
    dataset:
      type: openai_humaneval
      name: HumanEval
    metrics:
    - name: pass@1
      type: pass@1
      value: 0.0
      verified: false
---

# Mistral-7b-OpenOrca-lora

**This is a test.**


This LoRA model is extracted from the efficient parameter fine-tuned model ([Mistral-7B-OpenOra](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)), 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](https://huggingface.co/uukuguy/Mistral-7B-OpenOrca-lora-merged)

The source code is [here](https://github.com/uukuguy/multi_loras)

## lm-evaluation-harness

[Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)

| Metric | Mistral-7B-OpenOrca | Mistral-7B-OpenOrca-lora|
| --- | --- |--- |
| ARC | 64.08 | |
| HellaSwag | 83.99 | |
| MMLU | 62.24 | |
| TruthfulQA | 53.05 | |
| Average | 65.84 | |

## HumanEval

| Metric | Mistral-7B-OpenOrca | Mistral-7B-OpenOrca-lora|
| --- | --- | --- |
| 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