---
license: apache-2.0
metrics:
- accuracy
pipeline_tag: text-generation
---

## Summary

"Deer-3b," an instruction-following large language model based on "Bloom-3b," is fine-tuned using ±5k instructions.

Deer will also be available in larger models size.

## Usage

To use the model with the `transformers` library on a machine with GPUs.

```python
import torch
from transformers import pipeline
generate_text = pipeline(model="PSanni/Deer-3b", torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto")
```

You can then use the pipeline to answer instructions:

```python
res = generate_text("Explain to me the difference between nuclear fission and fusion.")
print(res[0]["generated_text"])
```

### Note:

Kindly note that the model isn't attuned to human preferences and could generate unsuitable, unethical, biased, and toxic responses. 
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_PSanni__Deer-3b)

| Metric                | Value                     |
|-----------------------|---------------------------|
| Avg.                  | 32.01   |
| ARC (25-shot)         | 38.48          |
| HellaSwag (10-shot)   | 57.41    |
| MMLU (5-shot)         | 25.64         |
| TruthfulQA (0-shot)   | 39.98   |
| Winogrande (5-shot)   | 57.46   |
| GSM8K (5-shot)        | 0.3        |
| DROP (3-shot)         | 4.83         |