Instructions to use Loom-Labs/Apollo-1-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Loom-Labs/Apollo-1-4B with Transformers:

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

pipe = pipeline("text-generation", model="Loom-Labs/Apollo-1-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Loom-Labs/Apollo-1-4B")
model = AutoModelForCausalLM.from_pretrained("Loom-Labs/Apollo-1-4B")
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

vLLM

How to use Loom-Labs/Apollo-1-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Loom-Labs/Apollo-1-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Loom-Labs/Apollo-1-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Loom-Labs/Apollo-1-4B

SGLang

How to use Loom-Labs/Apollo-1-4B 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 "Loom-Labs/Apollo-1-4B" \
    --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": "Loom-Labs/Apollo-1-4B",
		"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 "Loom-Labs/Apollo-1-4B" \
        --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": "Loom-Labs/Apollo-1-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Loom-Labs/Apollo-1-4B with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Loom-Labs/Apollo-1-4B to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Loom-Labs/Apollo-1-4B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Loom-Labs/Apollo-1-4B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Loom-Labs/Apollo-1-4B",
    max_seq_length=2048,
)

Docker Model Runner
How to use Loom-Labs/Apollo-1-4B with Docker Model Runner:
```
docker model run hf.co/Loom-Labs/Apollo-1-4B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Apollo-1-4B

Apollo-1-4B is a 4 billion parameter instruction-tuned model developed by Noema Research.
It is based on Qwen3-4B and optimized for reasoning, instruction following, and lightweight deployment at scale.

This model represents the mid-size member of the Apollo series, balancing performance and efficiency for a broad range of use cases.

Model Overview

Base model: Qwen3-4B
Architecture: Decoder-only transformer
Parameters: ~4B
Context length: up to 32k tokens (inherits Qwen3 long-context support)
Domain: General-purpose reasoning and instruction following
Primary applications:
- Conversational AI
- Multi-step reasoning tasks
- Education and tutoring systems
- Knowledge assistants and prototyping agents
License: anvdl-1.0

Key Features

Instruction tuning for consistent conversational and task-oriented responses
Improved reasoning depth compared to Apollo-1-2B, enabling stronger performance on complex queries
Long-context handling, inherited from Qwen3 architecture
Multilingual coverage, retaining broad knowledge across languages
Balanced resource requirements, deployable on high-end consumer hardware and cloud GPUs

Usage

The model is available in Hugging Face Transformers format. Example:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "NoemaResearch/Apollo-1-4B"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

messages = [
    {"role":"system", "content":"You are Apollo, a helpful reasoning assistant."},
    {"role":"user", "content":"Summarize the main differences between reinforcement learning and supervised learning."}
]

inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=768, temperature=0.6, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Recommended settings:

temperature=0.4–0.8
top_p=0.9–0.95
Lower temperatures yield more factual and concise answers

Evaluation

Apollo-1-4B demonstrates stronger reasoning capabilities relative to Apollo-1-2B, with internal evaluations indicating:

Higher accuracy on step-by-step reasoning tasks
More robust instruction adherence
Reduced hallucinations in factual settings
Effective balance between performance and efficiency

A full benchmark report will be provided in a future update. For upstream performance details, see the Qwen3-4B model card.

Limitations

Reasoning scale: While improved, Apollo-1-4B cannot match larger models (14B+) on complex or open-ended tasks
Knowledge breadth: Some specialized or domain-specific knowledge remains limited
Hallucinations: May generate plausible but incorrect information
Prompt sensitivity: Outputs remain dependent on careful prompt formulation

Responsible Use

Do not rely on Apollo-1-4B for critical decisions without human oversight
Verify outputs before applying in factual, legal, or safety-critical contexts
Avoid providing personal or sensitive data in prompts
The model should not be used to generate unsafe, harmful, or disallowed content

Model Variants

Full precision (safetensors) — research and high-fidelity inference
bf16 / fp16 — efficient inference on modern accelerators
Quantized versions (int8 / int4) — deployment in resource-constrained environments

Citation

If you use this model, please cite both Apollo-1-4B and the Qwen3 base model:

@misc{noema2025apollo4b,
  title={Apollo-1-4B},
  author={Noema Research},
  year={2025},
  howpublished={\url{https://huggingface.co/NoemaResearch/Apollo-1-4B}}
}