Instructions to use soyrsoyr/erebus-v2-1.5b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use soyrsoyr/erebus-v2-1.5b-base with Transformers:

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

pipe = pipeline("text-generation", model="soyrsoyr/erebus-v2-1.5b-base")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("soyrsoyr/erebus-v2-1.5b-base")
model = AutoModelForCausalLM.from_pretrained("soyrsoyr/erebus-v2-1.5b-base", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use soyrsoyr/erebus-v2-1.5b-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "soyrsoyr/erebus-v2-1.5b-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "soyrsoyr/erebus-v2-1.5b-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/soyrsoyr/erebus-v2-1.5b-base

SGLang

How to use soyrsoyr/erebus-v2-1.5b-base 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 "soyrsoyr/erebus-v2-1.5b-base" \
    --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": "soyrsoyr/erebus-v2-1.5b-base",
		"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 "soyrsoyr/erebus-v2-1.5b-base" \
        --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": "soyrsoyr/erebus-v2-1.5b-base",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use soyrsoyr/erebus-v2-1.5b-base with Docker Model Runner:
```
docker model run hf.co/soyrsoyr/erebus-v2-1.5b-base
```

Erebus v2 1.5B — Base

A 1.5B parameter causal language model pretrained from scratch using the Qwen3 architecture. This is the base (pretrained) model — see the instruct and tool-calling variants for fine-tuned versions.

Model Details


Parameters	1.72B
Architecture	Qwen3 (28 layers, 24 heads, d_model=2048)
Vocab size	151,669 (Qwen3 tokenizer)
Context length	2,048 tokens
Precision	bf16
Training tokens	5.5B
Final loss	~2.37

Training

Hardware: 4x NVIDIA A100-SXM4-80GB
Framework: PyTorch + Accelerate + Flash Attention 2
Optimizer: AdamW (fused), β1=0.9, β2=0.95, ε=1e-8
Learning rate: 4e-4 peak, WSD schedule (warmup → stable → decay)
Warmup: 150 steps (linear)
Batch size: 8 per device × 4 GPUs × 32 gradient accumulation = 1,024 sequences
Sequence length: 2,048 tokens (packed)
Weight decay: 0.1
Max grad norm: 1.0
Total steps: 2,644

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "soyrsoyr/erebus-v2-1.5b-base",
    torch_dtype="bfloat16",
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("soyrsoyr/erebus-v2-1.5b-base")

inputs = tokenizer("The future of AI is", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Variants

Variant	Description	Link
Base	Pretrained model (this)	soyrsoyr/erebus-v2-1.5b-base
Instruct	SFT on SmolTalk for chat/instruction following	soyrsoyr/erebus-v2-1.5b-instruct
Tool	SFT on xLAM for function calling	soyrsoyr/erebus-v2-1.5b-tool

Limitations

This is a small (1.5B) research model trained on a limited token budget (5.5B tokens). It is not intended for production use. Outputs may be incoherent, repetitive, or factually incorrect. The fine-tuned variants (instruct, tool) are significantly more useful for downstream tasks.

License

Apache 2.0

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Safetensors

Model size

2B params

Tensor type

BF16

Collection including soyrsoyr/erebus-v2-1.5b-base

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Collection

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