# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("PSanni/Deer-3b")
model = AutoModelForCausalLM.from_pretrained("PSanni/Deer-3b")Quick Links
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.
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:
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
Detailed results can be found here
| 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 |
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="PSanni/Deer-3b")