100 lines
2.8 KiB
Markdown
100 lines
2.8 KiB
Markdown
---
|
|
license: apache-2.0
|
|
datasets:
|
|
- GAIR/lima
|
|
language:
|
|
- en
|
|
pipeline_tag: text-generation
|
|
thumbnail: https://huggingface.co/mrm8488/limstral-7B-v0.1/resolve/main/limstral_logo.png
|
|
---
|
|
# 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 |