Instructions to use evox-os/axiom-1-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use evox-os/axiom-1-hf with PEFT:
```
Task type is invalid.
```

How to use evox-os/axiom-1-hf with Transformers:

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

pipe = pipeline("text-generation", model="evox-os/axiom-1-hf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("evox-os/axiom-1-hf")
model = AutoModelForCausalLM.from_pretrained("evox-os/axiom-1-hf", 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 evox-os/axiom-1-hf with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/evox-os/axiom-1-hf

SGLang

How to use evox-os/axiom-1-hf 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 "evox-os/axiom-1-hf" \
    --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": "evox-os/axiom-1-hf",
		"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 "evox-os/axiom-1-hf" \
        --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": "evox-os/axiom-1-hf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use evox-os/axiom-1-hf with Docker Model Runner:
```
docker model run hf.co/evox-os/axiom-1-hf
```

AXIOM (adaptación evox-os/axiom-1-hf)

Adaptación LoRA entrenada y curada por AXIOM / EVOX.OS sobre Qwen/Qwen2.5-0.5B-Instruct.

Datos de entrenamiento

Distribución interna del flywheel de AXIOM (SFT/DPO)

Notas

Regla R1 (soberanía): estos pesos se usan para DESARROLLAR y ENTRENAR. En producto, AXIOM corre en hardware del usuario (WebGPU / Ollama / AXIOM Core).
Inferencia local recomendada: Ollama, vLLM o transformers en la propia máquina.

Uso (local)

from peft import AutoPeftModelForCausalLM
model = AutoPeftModelForCausalLM.from_pretrained("evox-os/axiom-1-hf")

Este modelo es un artefacto de capacitación, no la identidad completa de AXIOM.

Downloads last month: -

Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for evox-os/axiom-1-hf

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Adapter

(800)

this model