Azrail/smallm_70

SmalLM

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SmalLM is a series of small transformer models built from scratch for language modeling. This project is designed to explore innovative approaches to transformer architectures through modular pipelines for pretraining, fine-tuning, and alignment.

Uses

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Azrail/smallm_70")
model = AutoModelForCausalLM.from_pretrained("Azrail/smallm_70", trust_remote_code=True)
inputs = tokenizer("How are you?", return_tensors="pt")

out = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.batch_decode(out))

Model Details**

Key Features:

1. Grouped Query Attention (GQA).

2. Mixture-of-Experts with auxiliary loss-free balancing.

3. ALiBi (Attention with Linear Biases) or Rotary Position Embedding (RoPE).

4. NTK-by-parts RoPE interpolation for extends context length.

Pre-Training:

Model	Training Data	Steps	Content Length	Tokens	LR	Batch Size	Precision
SmalLM-70M	smollm-corpus	70k	1024	18B	1e-3	0.25M	bfloat16
SmalLM-150M	smollm-corpus	-	1024	-	-	-	bfloat16
SmalLM-350M	smollm-corpus	-	1024	-	-	-	bfloat16
SmalLM-500M	smollm-corpus	-	1024	-	-	-	bfloat16

Evaluation: Evaluation runing with lm-evaluation-harness

Model	MMLU	ARC easy/hard	PIQA	HellaSwag	OBQA	Winogrande
SmalLM-70M	25.33	51.47/25.68	61.75	30.31	30.8	50.83
SmalLM-150M	-	-	-	-	-	-
SmalLM-350M	-	-	-	-	-	-
SmalLM-500M	-	-	-	-	-	-

Procedure:

Framework versions

• Transformers 4.50.3
• Pytorch 2.6.0+cu126
• Datasets 3.5.0
• Tokenizers 0.21.1