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---
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 |
|