
## 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         |

{"license": "apache-2.0", "metrics": ["accuracy"], "pipeline_tag": "text-generation"}
transformers pytorch bloom text-generation license:apache-2.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #bloom #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

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.


You can then use the pipeline to answer instructions:


### 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
=======================================


Detailed results can be found here




### 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
=======================================


Detailed results can be found here
TAGS
#transformers #pytorch #bloom #text-generation #license-apache-2.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 ### 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
=======================================


Detailed results can be found here