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

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

Model description

This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the English 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

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

Detailed results can be found here