---
language:
- en
license: apache-2.0
library_name: peft
tags:
- mistral
- generated_from_trainer
- Transformers
- text-generation-inference
datasets:
- robinsmits/ChatAlpaca-20K
inference: false
base_model: mistralai/Mistral-7B-Instruct-v0.2
pipeline_tag: text-generation
model-index:
- name: Mistral-Instruct-7B-v0.2-ChatAlpaca
  results: []
---

# Mistral-Instruct-7B-v0.2-ChatAlpaca

## Model description

This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the English [robinsmits/ChatAlpaca-20K](https://www.huggingface.co/datasets/robinsmits/ChatAlpaca-20K) dataset.

It achieves the following results on the evaluation set:
- Loss: 0.8584

## Model usage

A basic example of how to use the finetuned model. Note this example is a modified version from the base model.

```
import torch
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

device = "cuda"

model = AutoPeftModelForCausalLM.from_pretrained("robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpaca", 
                                                 device_map = "auto", 
                                                 load_in_4bit = True, 
                                                 torch_dtype = torch.bfloat16)

tokenizer = AutoTokenizer.from_pretrained("robinsmits/Mistral-Instruct-7B-v0.2-ChatAlpaca")

messages = [
    {"role": "user", "content": "What is your favourite condiment?"},
    {"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
    {"role": "user", "content": "Do you have mayonnaise recipes?"}
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors = "pt")

generated_ids = model.generate(input_ids = encodeds.to(device), max_new_tokens = 512, do_sample = True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.99          | 0.2   | 120  | 0.9355          |
| 0.8793        | 0.39  | 240  | 0.8848          |
| 0.8671        | 0.59  | 360  | 0.8737          |
| 0.8662        | 0.78  | 480  | 0.8679          |
| 0.8627        | 0.98  | 600  | 0.8639          |
| 0.8426        | 1.18  | 720  | 0.8615          |
| 0.8574        | 1.37  | 840  | 0.8598          |
| 0.8473        | 1.57  | 960  | 0.8589          |
| 0.8528        | 1.76  | 1080 | 0.8585          |
| 0.852         | 1.96  | 1200 | 0.8584          |


### Framework versions

- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
# [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_robinsmits__Mistral-Instruct-7B-v0.2-ChatAlpaca)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |61.21|
|AI2 Reasoning Challenge (25-Shot)|56.74|
|HellaSwag (10-Shot)              |80.82|
|MMLU (5-Shot)                    |59.10|
|TruthfulQA (0-shot)              |55.86|
|Winogrande (5-shot)              |77.11|
|GSM8k (5-shot)                   |37.60|