Instructions to use Azrail/smallm_70 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Azrail/smallm_70 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Azrail/smallm_70", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Azrail/smallm_70", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Azrail/smallm_70 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Azrail/smallm_70"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azrail/smallm_70",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Azrail/smallm_70

SGLang

How to use Azrail/smallm_70 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Azrail/smallm_70" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azrail/smallm_70",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Azrail/smallm_70" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azrail/smallm_70",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Azrail/smallm_70 with Docker Model Runner:
```
docker model run hf.co/Azrail/smallm_70
```

smallm_70 / README.md

Azrail

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	---
	library_name: transformers
	license: mit
	datasets:
	- YourDatasetName/if-applicable
	language:
	- en
	pipeline_tag: text-generation
	tags:
	- transformer
	- language-model
	- experimental
	---

	# SmalLM

	<hr>
	<div align="center">
	<a href="https://github.com/azrails/SmalLm" target="_blank" style="margin: 2px;">
	<img alt="GitHub" src="https://img.shields.io/badge/GitHub-SmalLM-181717?logo=github" style="display: inline-block; vertical-align: middle;"/>
	</a>
	<a href="https://github.com/azrails/SmalLm/blob/main/LICENSE" style="margin: 2px;">
	<img alt="License" src="https://img.shields.io/badge/License-MIT-blue.svg" style="display: inline-block; vertical-align: middle;"/>
	</a>
	</div>

	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

	```python
	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](https://huggingface.co/Azrail/smallm_70) \| [smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) \| 70k \| 1024 \| 18B \| 1e-3 \| 0.25M \| bfloat16 \|
	\| [SmalLM-150M](#) \| [smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) \| - \| 1024 \| - \| - \| - \| bfloat16 \|
	\| [SmalLM-350M](#) \| [smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) \| - \| 1024 \| - \| - \| - \| bfloat16 \|
	\| [SmalLM-500M](#) \| [smollm-corpus](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) \| - \| 1024 \| - \| - \| - \| bfloat16 \|

	Evaluation:
	Evaluation runing with [lm-evaluation-harness](https://github.com/EleutherAI/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:

	[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://api.wandb.ai/links/azrails-main/58rwb1yb)

	### Framework versions

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