metadata
license: apache-2.0
datasets:
- HuggingFaceH4/no_robots
base_model: mistralai/Mistral-7B-v0.1
language:
- en
pipeline_tag: text-generation
thumbnail: >-
https://huggingface.co/mrm8488/mistral-7b-ft-h4-no_robots_instructions/resolve/main/mistralh4-removebg-preview.png?download=true
Mistral 7B fine-tuned on H4/No Robots instructions
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the HuggingFaceH4/no_robots 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 one epoch 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: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
- mixed_precision_training: Native AMP
Training results
| Step | Training Loss | Validation Loss |
|---|---|---|
| 10 | 1.796200 | 1.774305 |
| 20 | 1.769700 | 1.679720 |
| 30 | 1.626800 | 1.667754 |
| 40 | 1.663400 | 1.665188 |
| 50 | 1.565700 | 1.659000 |
| 60 | 1.660300 | 1.658270 |
Usage
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
repo_id = "mrm8488/mistral-7b-ft-h4-no_robots_instructions"
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, eos_token_id=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