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---
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|
|