---
license: apache-2.0
library_name: transformers
tags:
- mistral
- alpaca
datasets:
- tatsu-lab/alpaca
pipeline_tag: text-generation
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: Mistral-7B-Alpaca-52k-v0.1
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: AI2 Reasoning Challenge (25-Shot)
      type: ai2_arc
      config: ARC-Challenge
      split: test
      args:
        num_few_shot: 25
    metrics:
    - type: acc_norm
      value: 60.92
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Mistral-7B-Alpaca-52k-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: HellaSwag (10-Shot)
      type: hellaswag
      split: validation
      args:
        num_few_shot: 10
    metrics:
    - type: acc_norm
      value: 82.13
      name: normalized accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Mistral-7B-Alpaca-52k-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: MMLU (5-Shot)
      type: cais/mmlu
      config: all
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 63.41
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Mistral-7B-Alpaca-52k-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: TruthfulQA (0-shot)
      type: truthful_qa
      config: multiple_choice
      split: validation
      args:
        num_few_shot: 0
    metrics:
    - type: mc2
      value: 41.5
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Mistral-7B-Alpaca-52k-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: Winogrande (5-shot)
      type: winogrande
      config: winogrande_xl
      split: validation
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 77.35
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Mistral-7B-Alpaca-52k-v0.1
      name: Open LLM Leaderboard
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      name: GSM8k (5-shot)
      type: gsm8k
      config: main
      split: test
      args:
        num_few_shot: 5
    metrics:
    - type: acc
      value: 37.45
      name: accuracy
    source:
      url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=MaziyarPanahi/Mistral-7B-Alpaca-52k-v0.1
      name: Open LLM Leaderboard
---

# Description

`mistralai/Mistral-7B-v0.1` model fine-tuned over 52k alpaca dataset

# How to use it

```python
# pip install transformers==4.35.2
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
from transformers import pipeline

model_id="MaziyarPanahi/Mistral-7B-Alpaca-52k-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)

streamer = TextStreamer(tokenizer)

model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=1024,
    temperature=0.1,
    do_sample=True,
    top_p=0.95,
    repetition_penalty=1.15,
    return_full_text=False,
    streamer=streamer
)

prompt = """Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
describe about pros and cons of docker system. Answer in bullet point

### Response:
"""

res = pipe(prompt)[0]['generated_text']
```

Results:

```
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
describe about pros and cons of docker system. Answer in bullet point

### Response:
Pros of Docker System: 
- Improved portability - Docker containers can be easily moved between different environments, making it easier to deploy applications across multiple platforms.
- Increased security - Containers are isolated from each other, which helps prevent malicious code from spreading throughout the system.
- Better resource utilization - Containers allow for better resource management by allowing users to run multiple applications on a single host without having to worry about conflicts or performance issues.

Cons of Docker System: 
- Learning curve - It takes time to learn how to use Docker effectively, as there are many commands and concepts involved.
- Limited customization options - While Docker provides some basic configuration options, more advanced features such as network routing require additional tools.
- Performance overhead - Running multiple containers on a single host may result in slower performance due to increased memory usage.</s>
```

## Eval

```python
{
    "all": {
        "acc": 0.6309850839451187,
        "acc_stderr": 0.032333688535613636,
        "acc_norm": 0.6368691004374645,
        "acc_norm_stderr": 0.03298401757997533,
        "mc1": 0.29008567931456547,
        "mc1_stderr": 0.01588623687420952,
        "mc2": 0.41501661742948026,
        "mc2_stderr": 0.014285902986671931
    },
    "harness|arc:challenge|25": {
        "acc": 0.5750853242320819,
        "acc_stderr": 0.014445698968520767,
        "acc_norm": 0.6092150170648464,
        "acc_norm_stderr": 0.01425856388051378
    },
    "harness|hellaswag|10": {
        "acc": 0.6221868153754232,
        "acc_stderr": 0.0048384969668239025,
        "acc_norm": 0.8212507468631747,
        "acc_norm_stderr": 0.0038235918141330347
    },
    "harness|hendrycksTest-abstract_algebra|5": {
        "acc": 0.32,
        "acc_stderr": 0.046882617226215034,
        "acc_norm": 0.32,
        "acc_norm_stderr": 0.046882617226215034
    },
    "harness|hendrycksTest-anatomy|5": {
        "acc": 0.6,
        "acc_stderr": 0.04232073695151589,
        "acc_norm": 0.6,
        "acc_norm_stderr": 0.04232073695151589
    },
    "harness|hendrycksTest-astronomy|5": {
        "acc": 0.6447368421052632,
        "acc_stderr": 0.038947344870133176,
        "acc_norm": 0.6447368421052632,
        "acc_norm_stderr": 0.038947344870133176
    },
    "harness|hendrycksTest-business_ethics|5": {
        "acc": 0.57,
        "acc_stderr": 0.04975698519562428,
        "acc_norm": 0.57,
        "acc_norm_stderr": 0.04975698519562428
    },
    "harness|hendrycksTest-clinical_knowledge|5": {
        "acc": 0.6792452830188679,
        "acc_stderr": 0.02872750295788027,
        "acc_norm": 0.6792452830188679,
        "acc_norm_stderr": 0.02872750295788027
    },
    "harness|hendrycksTest-college_biology|5": {
        "acc": 0.7430555555555556,
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        "acc_norm": 0.7430555555555556,
        "acc_norm_stderr": 0.03653946969442099
    },
    "harness|hendrycksTest-college_chemistry|5": {
        "acc": 0.49,
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        "acc_norm": 0.49,
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    },
    "harness|hendrycksTest-college_computer_science|5": {
        "acc": 0.56,
        "acc_stderr": 0.04988876515698589,
        "acc_norm": 0.56,
        "acc_norm_stderr": 0.04988876515698589
    },
    "harness|hendrycksTest-college_mathematics|5": {
        "acc": 0.36,
        "acc_stderr": 0.048241815132442176,
        "acc_norm": 0.36,
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    },
    "harness|hendrycksTest-college_medicine|5": {
        "acc": 0.653179190751445,
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        "acc_norm": 0.653179190751445,
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    },
    "harness|hendrycksTest-college_physics|5": {
        "acc": 0.4019607843137255,
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        "acc_norm": 0.4019607843137255,
        "acc_norm_stderr": 0.048786087144669955
    },
    "harness|hendrycksTest-computer_security|5": {
        "acc": 0.79,
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        "acc_norm": 0.79,
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    },
    "harness|hendrycksTest-conceptual_physics|5": {
        "acc": 0.5702127659574469,
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        "acc_norm": 0.5702127659574469,
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    },
    "harness|hendrycksTest-econometrics|5": {
        "acc": 0.49122807017543857,
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    "harness|hendrycksTest-electrical_engineering|5": {
        "acc": 0.5862068965517241,
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    },
    "harness|hendrycksTest-elementary_mathematics|5": {
        "acc": 0.3915343915343915,
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        "acc_norm": 0.3915343915343915,
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    },
    "harness|hendrycksTest-formal_logic|5": {
        "acc": 0.4444444444444444,
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        "acc_norm": 0.4444444444444444,
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    },
    "harness|hendrycksTest-global_facts|5": {
        "acc": 0.32,
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    },
    "harness|hendrycksTest-high_school_biology|5": {
        "acc": 0.7419354838709677,
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        "acc_norm": 0.7419354838709677,
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    },
    "harness|hendrycksTest-high_school_chemistry|5": {
        "acc": 0.5024630541871922,
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        "acc_norm": 0.5024630541871922,
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    },
    "harness|hendrycksTest-high_school_computer_science|5": {
        "acc": 0.67,
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        "acc_norm": 0.67,
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    },
    "harness|hendrycksTest-high_school_european_history|5": {
        "acc": 0.7575757575757576,
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    },
    "harness|hendrycksTest-high_school_geography|5": {
        "acc": 0.7929292929292929,
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    },
    "harness|hendrycksTest-high_school_government_and_politics|5": {
        "acc": 0.8601036269430051,
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        "acc_norm": 0.8601036269430051,
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    },
    "harness|hendrycksTest-high_school_macroeconomics|5": {
        "acc": 0.6358974358974359,
        "acc_stderr": 0.024396672985094764,
        "acc_norm": 0.6358974358974359,
        "acc_norm_stderr": 0.024396672985094764
    },
    "harness|hendrycksTest-high_school_mathematics|5": {
        "acc": 0.362962962962963,
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        "acc_norm": 0.362962962962963,
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    },
    "harness|hendrycksTest-high_school_microeconomics|5": {
        "acc": 0.6218487394957983,
        "acc_stderr": 0.03149930577784906,
        "acc_norm": 0.6218487394957983,
        "acc_norm_stderr": 0.03149930577784906
    },
    "harness|hendrycksTest-high_school_physics|5": {
        "acc": 0.32450331125827814,
        "acc_stderr": 0.038227469376587525,
        "acc_norm": 0.32450331125827814,
        "acc_norm_stderr": 0.038227469376587525
    },
    "harness|hendrycksTest-high_school_psychology|5": {
        "acc": 0.8146788990825689,
        "acc_stderr": 0.016659279700295838,
        "acc_norm": 0.8146788990825689,
        "acc_norm_stderr": 0.016659279700295838
    },
    "harness|hendrycksTest-high_school_statistics|5": {
        "acc": 0.49537037037037035,
        "acc_stderr": 0.03409825519163572,
        "acc_norm": 0.49537037037037035,
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    },
    "harness|hendrycksTest-high_school_us_history|5": {
        "acc": 0.7892156862745098,
        "acc_stderr": 0.028626547912437406,
        "acc_norm": 0.7892156862745098,
        "acc_norm_stderr": 0.028626547912437406
    },
    "harness|hendrycksTest-high_school_world_history|5": {
        "acc": 0.7552742616033755,
        "acc_stderr": 0.027985699387036423,
        "acc_norm": 0.7552742616033755,
        "acc_norm_stderr": 0.027985699387036423
    },
    "harness|hendrycksTest-human_aging|5": {
        "acc": 0.6636771300448431,
        "acc_stderr": 0.031708824268455,
        "acc_norm": 0.6636771300448431,
        "acc_norm_stderr": 0.031708824268455
    },
    "harness|hendrycksTest-human_sexuality|5": {
        "acc": 0.7862595419847328,
        "acc_stderr": 0.0359546161177469,
        "acc_norm": 0.7862595419847328,
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    },
    "harness|hendrycksTest-international_law|5": {
        "acc": 0.7933884297520661,
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    },
    "harness|hendrycksTest-jurisprudence|5": {
        "acc": 0.7592592592592593,
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        "acc_norm": 0.7592592592592593,
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    },
    "harness|hendrycksTest-logical_fallacies|5": {
        "acc": 0.803680981595092,
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        "acc_norm": 0.803680981595092,
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    "harness|hendrycksTest-machine_learning|5": {
        "acc": 0.5178571428571429,
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    },
    "harness|hendrycksTest-management|5": {
        "acc": 0.8252427184466019,
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        "acc_norm_stderr": 0.03760178006026621
    },
    "harness|hendrycksTest-marketing|5": {
        "acc": 0.8632478632478633,
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        "acc_norm": 0.8632478632478633,
        "acc_norm_stderr": 0.022509033937077816
    },
    "harness|hendrycksTest-medical_genetics|5": {
        "acc": 0.74,
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        "acc_norm": 0.74,
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    },
    "harness|hendrycksTest-miscellaneous|5": {
        "acc": 0.8173690932311622,
        "acc_stderr": 0.013816335389973136,
        "acc_norm": 0.8173690932311622,
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    "harness|hendrycksTest-moral_disputes|5": {
        "acc": 0.7023121387283237,
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    "harness|hendrycksTest-moral_scenarios|5": {
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    "harness|hendrycksTest-nutrition|5": {
        "acc": 0.7450980392156863,
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    },
    "harness|hendrycksTest-philosophy|5": {
        "acc": 0.7106109324758842,
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    "harness|hendrycksTest-prehistory|5": {
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    "harness|hendrycksTest-professional_accounting|5": {
        "acc": 0.4716312056737589,
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    "harness|hendrycksTest-professional_law|5": {
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    "harness|hendrycksTest-professional_medicine|5": {
        "acc": 0.6580882352941176,
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    "harness|hendrycksTest-professional_psychology|5": {
        "acc": 0.6519607843137255,
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        "acc_norm_stderr": 0.019270998708223974
    },
    "harness|hendrycksTest-public_relations|5": {
        "acc": 0.6636363636363637,
        "acc_stderr": 0.04525393596302506,
        "acc_norm": 0.6636363636363637,
        "acc_norm_stderr": 0.04525393596302506
    },
    "harness|hendrycksTest-security_studies|5": {
        "acc": 0.7224489795918367,
        "acc_stderr": 0.028666857790274645,
        "acc_norm": 0.7224489795918367,
        "acc_norm_stderr": 0.028666857790274645
    },
    "harness|hendrycksTest-sociology|5": {
        "acc": 0.8557213930348259,
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        "acc_norm": 0.8557213930348259,
        "acc_norm_stderr": 0.02484575321230604
    },
    "harness|hendrycksTest-us_foreign_policy|5": {
        "acc": 0.86,
        "acc_stderr": 0.03487350880197771,
        "acc_norm": 0.86,
        "acc_norm_stderr": 0.03487350880197771
    },
    "harness|hendrycksTest-virology|5": {
        "acc": 0.5481927710843374,
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        "acc_norm": 0.5481927710843374,
        "acc_norm_stderr": 0.03874371556587953
    },
    "harness|hendrycksTest-world_religions|5": {
        "acc": 0.8421052631578947,
        "acc_stderr": 0.027966785859160896,
        "acc_norm": 0.8421052631578947,
        "acc_norm_stderr": 0.027966785859160896
    },
    "harness|truthfulqa:mc|0": {
        "mc1": 0.29008567931456547,
        "mc1_stderr": 0.01588623687420952,
        "mc2": 0.41501661742948026,
        "mc2_stderr": 0.014285902986671931
    },
    "harness|winogrande|5": {
        "acc": 0.7734806629834254,
        "acc_stderr": 0.011764149054698332
    },
    "harness|gsm8k|5": {
        "acc": 0.37452615617892343,
        "acc_stderr": 0.013331774158491393
    }
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_MaziyarPanahi__Mistral-7B-Alpaca-52k-v0.1)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |60.46|
|AI2 Reasoning Challenge (25-Shot)|60.92|
|HellaSwag (10-Shot)              |82.13|
|MMLU (5-Shot)                    |63.41|
|TruthfulQA (0-shot)              |41.50|
|Winogrande (5-shot)              |77.35|
|GSM8k (5-shot)                   |37.45|