---
library_name: transformers
tags: [causal-lm, bloom, lora, peft, finetuning, english]
---

# Model Card for Jay24-AI/bloom-7b1-lora-tagger

This model is a **LoRA fine-tuned version of BigScience’s BLOOM-7B1** model, trained on a dataset of English quotes. The goal was to adapt BLOOM using the [PEFT](https://github.com/huggingface/peft) (Parameter-Efficient Fine-Tuning) approach with [LoRA](https://arxiv.org/abs/2106.09685), making it lightweight to train and efficient for deployment.

## Model Details

### Model Description

- **Developed by:** Jay24-AI
- **Funded by [optional]:** N/A
- **Shared by [optional]:** Jay24-AI
- **Model type:** Causal Language Model with LoRA adapters
- **Language(s):** English
- **License:** Apache-2.0 (inherited from `bigscience/bloom-7b1`; LoRA adapters are MIT-compatible)
- **Finetuned from model:** [bigscience/bloom-7b1](https://huggingface.co/bigscience/bloom-7b1)

### Model Sources

- **Repository:** https://huggingface.co/Jay24-AI/bloom-7b1-lora-tagger

## Uses

### Direct Use

The model can be used for **text generation and tagging** based on quote-like prompts. For example, you can input a quote, and the model will generate descriptive tags.

### Downstream Use

- Can be further fine-tuned on custom tagging or classification datasets.
- Could be integrated into applications that require lightweight **quote classification**, **text annotation**, or **prompt-based generation**.

### Out-of-Scope Use

- Not suitable for factual question answering.
- Not designed for sensitive or high-stakes decision-making (e.g., medical, legal, or financial advice).

## Bias, Risks, and Limitations

- Inherits limitations and biases from **BLOOM-7B1** (trained on large-scale internet data).
- The fine-tuned dataset (`Abirate/english_quotes`) is relatively small, so the model may overfit and generalize poorly outside similar data.
- Risk of generating irrelevant or biased tags if prompted outside the intended scope.
- Limited training (50 steps) may result in suboptimal performance.

### Recommendations

Users should:

- Validate outputs before production use.
- Avoid relying on the model for critical applications.

## How to Get Started with the Model

```python
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

peft_model_id = "Jay24-AI/bloom-7b1-lora-tagger"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)

batch = tokenizer("“The only way to do great work is to love what you do.” ->: ", return_tensors='pt')

with torch.cuda.amp.autocast():
  output_tokens = model.generate(**batch, max_new_tokens=50)

print('\n\n', tokenizer.decode(output_tokens[0], skip_special_tokens=True))
```

## Training Details

### Training Data

- **Dataset used:** [Abirate/english_quotes](https://huggingface.co/datasets/Abirate/english_quotes)
- **Subset:** Entire training split (exact size not specified in script).
- **Structure:** Each entry includes a `quote` and its corresponding `tags`.
- **Preprocessing:**
  - Combined the `quote` and `tags` into a single text string: `<quote> ->: <tags>`
  - Tokenized using the `AutoTokenizer` from **bigscience/bloom-7b1**.
  - Applied batching via Hugging Face `datasets.map` with `batched=True`.

### Training Procedure

#### Preprocessing

- Converted text examples into the `"quote ->: tags"` format.
- Tokenized using Bloom’s tokenizer with default settings.
- Applied `DataCollatorForLanguageModeling` with `mlm=False` (causal LM objective).

#### Training Hyperparameters

- **Base model:** bigscience/bloom-7b1
- **Adapter method:** LoRA via PEFT
- **LoRA configuration:**
  - `r`: 8
  - `lora_alpha`: 16
  - `lora_dropout`: 0.05
  - `bias`: "none"
  - `task_type`: "CAUSAL_LM"
- **TrainingArguments:**
  - `per_device_train_batch_size`: 2
  - `gradient_accumulation_steps`: 2
  - `warmup_steps`: 100
  - `max_steps`: 50
  - `learning_rate`: 2e-4
  - `fp16`: True
  - `logging_steps`: 1
  - `output_dir`: `outputs/`
- **Precision regime:** Mixed precision (fp16) with 8-bit quantization via `bitsandbytes`.
- **Caching:** `model.config.use_cache = False` during training to suppress warnings.
- **Additional Settings:**
  - Original model weights frozen; small parameters (e.g., layer normalization) cast to FP32 for stability.
  - Gradient checkpointing enabled to reduce memory usage.
  - `lm_head` modified to output FP32 for stability.

#### Hyperparameter Summary

| Hyperparameter              | Value                  |
|-----------------------------|------------------------|
| Base model                  | bigscience/bloom-7b1   |
| Adapter method              | LoRA (via PEFT)        |
| LoRA r                      | 8                      |
| LoRA alpha                  | 16                     |
| LoRA dropout                | 0.05                   |
| Bias                        | none                   |
| Task type                   | Causal LM              |
| Batch size (per device)     | 2                      |
| Gradient accumulation steps | 2                      |
| Effective batch size        | 4                      |
| Warmup steps                | 100                    |
| Max steps                   | 50                     |
| Learning rate               | 2e-4                   |
| Precision                   | fp16 (mixed precision) |
| Quantization                | 8-bit (bitsandbytes)   |
| Logging steps               | 1                      |
| Output directory            | outputs/               |
| Gradient checkpointing      | Enabled                |
| Use cache                   | False (during training)|

### Speeds, Sizes, Times

- **Trainable parameters:** LoRA adapters only (~0.1% of BLOOM-7B1’s ~7.1 billion parameters, exact count printed via `print_trainable_parameters`).
- **Approx. size:** Much smaller than 7B full checkpoint since only adapters are stored.
- **Max steps:** 50 (~100 updates with gradient accumulation).
- **Training runtime:** Not logged in script; depends on GPU.
- **Batch size effective:** 4 (2 × accumulation steps of 2).

### Compute Infrastructure

- **Hardware:** Single CUDA GPU (set with `os.environ["CUDA_VISIBLE_DEVICES"]="0"`; specific GPU model not specified, e.g., A100, T4, V100).
- **Software:**
  - PyTorch
  - Hugging Face Transformers (main branch from GitHub)
  - Hugging Face PEFT (main branch from GitHub)
  - Hugging Face Datasets
  - Accelerate
  - Bitsandbytes (for 8-bit quantization)
- **Gradient checkpointing:** Enabled to save memory.
- **Mixed precision:** Enabled with fp16.
- **Quantization:** 8-bit with double quantization, `nf8` type, `torch.float16` compute dtype.

## Evaluation

### Testing Data

- Same dataset (`Abirate/english_quotes`).
- No held-out test set reported in training script.

### Metrics

- No formal metrics logged; evaluation was qualitative (checking generated tags).

### Results

- The model successfully learns to generate tags for English quotes after training, as demonstrated by the inference example.

## Environmental Impact

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).

- **Hardware Type:** CUDA Single GPU: T4
- **Cloud Provider:** Colab

## Technical Specifications

### Model Architecture and Objective

- Base model: **BLOOM-7B1**, causal language modeling objective.
- Fine-tuned with **LoRA adapters** using PEFT.

### Compute Infrastructure

- **Hardware:** Single GPU (CUDA device 0).
- **Software:**
  - PyTorch
  - Hugging Face Transformers
  - Hugging Face PEFT
  - Hugging Face Datasets
  - Accelerate
  - Bitsandbytes

## Citation

If you use this model, please cite:

```bibtex
@misc{jay24ai2025bloomlora,
  title={LoRA Fine-Tuned BLOOM-7B1 for Quote Tagging},
  author={Jay24-AI},
  year={2025},
  howpublished={\url{https://huggingface.co/Jay24-AI/bloom-7b1-lora-tagger}}
}
```

## Model Card Contact

For questions or issues, contact the maintainer via Hugging Face discussions: https://huggingface.co/Jay24-AI/bloom-7b1-lora-tagger/discussions