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---
language:
- en
license: apache-2.0
datasets:
- GAIR/lima
pipeline_tag: text-generation
thumbnail: https://huggingface.co/mrm8488/limstral-7B-v0.1/resolve/main/limstral_logo.png
base_model: mistralai/Mistral-7B-v0.1
---
# LIMSTRAL 🇲🍋
<div style="text-align:center;width:250px;height:250px;">
<img src="https://huggingface.co/mrm8488/limstral-7B-v0.1/resolve/main/limstral_logo-nb.png" alt="limstral logo"">
</div>
<br />
## Mistral 7B fine-tuned on LIMA
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the [LIMA](https://huggingface.co/datasets/GAIR/lima) dataset for instruction following downstream task.
## Training procedure
The model was loaded on **8 bits** and fine-tuned on the LIMA dataset using the **LoRA** PEFT technique with the `huggingface/peft` library and `trl/sft` for 2 epochs on 1 x A100 (40GB) GPU.
SFT Trainer params:
```
trainer = SFTTrainer(
model=model,
train_dataset=train_ds,
eval_dataset=test_ds,
peft_config=peft_config,
dataset_text_field="text",
max_seq_length=2048,
tokenizer=tokenizer,
args=training_arguments,
packing=False
)
```
LoRA config:
```
config = LoraConfig(
lora_alpha=16,
lora_dropout=0.1,
r=64,
bias="none",
task_type="CAUSAL_LM",
target_modules = ['q_proj', 'k_proj', 'down_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj']
)
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 66
- gradient_accumulation_steps: 64
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Step | Training Loss | Validation Loss |
|------|---------------|-----------------|
| 5 | 1.802800 | 1.848371 |
| 10 | 1.605800 | 1.803416 |
| 15 | 1.844800 | 1.762276 |
| 20 | 1.752600 | 1.754042 |
| 25 | 1.512400 | 1.750550 |
### Usage
```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
repo_id = "mrm8488/limstral-7B-v0.1"
model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained(repo_id)
gen = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0)
instruction = "[INST] Write an email to say goodbye to me boss [\INST]"
res = gen(instruction, max_new_tokens=512, temperature=0.3, top_p=0.75, top_k=40, repetition_penalty=1.2)
print(res[0]['generated_text'])
```
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1 |