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

Libraries

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

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

pipe = pipeline("text-generation", model="soyrsoyr/erebus-v2-1.5b-tool")
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-tool")
model = AutoModelForCausalLM.from_pretrained("soyrsoyr/erebus-v2-1.5b-tool", 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-tool 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-tool"
# 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-tool",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Docker Model Runner
How to use soyrsoyr/erebus-v2-1.5b-tool with Docker Model Runner:
```
docker model run hf.co/soyrsoyr/erebus-v2-1.5b-tool
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Erebus v2 1.5B - Tool

A 1.5B parameter function-calling model fine-tuned from erebus-v2-1.5b-base on Salesforce xLAM-60k for structured tool use.

Training


Base model	erebus-v2-1.5b-base (5.5B token pretrain)
SFT dataset	Salesforce/xlam-function-calling-60k (~60k examples)
Epochs	3
LR	2e-5 (cosine decay)
Batch	4 per device x 4 GPUs x 4 grad accum = 64
Steps	2,811
Time	2.8 hours on 4x A100-SXM4-80GB
Avg seq length	424 tokens

Findings

The model correctly selects the right tool from a schema and produces JSON function calls. Example outputs:

get_weather with {"location": "Paris"} - correct tool and argument
calculator with {"expression": "25 * 47"} - chose calculator over search
send_email with {"subject": "Hello"} - correct tool selection

Known Limitations

Schema leakage: Arguments sometimes include schema-like fields ("type": "object", "properties") instead of flat values.
Missing/wrong args: Occasionally drops required arguments or fills in placeholder values.
Post-JSON degeneration: After the initial JSON object, output degenerates into repetition. Use a stop token or extract the first JSON object.
Same base model limitations: Small pretraining budget (5.5B tokens) limits overall language understanding.

Best used with post-processing to extract the first JSON object from the output.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import json

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

messages = [
    {"role": "system", "content": 'You have access to: {"name": "get_weather", "parameters": {"properties": {"location": {"type": "string"}}}}'},
    {"role": "user", "content": "What's the weather in Tokyo?"},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
response = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
# Extract first JSON object from response
print(response.split("\n")[0])

Variants

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