Instructions to use fiel1986/Andromeda-Code-0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fiel1986/Andromeda-Code-0.5B with Transformers:

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

pipe = pipeline("text-generation", model="fiel1986/Andromeda-Code-0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fiel1986/Andromeda-Code-0.5B")
model = AutoModelForCausalLM.from_pretrained("fiel1986/Andromeda-Code-0.5B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fiel1986/Andromeda-Code-0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fiel1986/Andromeda-Code-0.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "fiel1986/Andromeda-Code-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fiel1986/Andromeda-Code-0.5B

SGLang

How to use fiel1986/Andromeda-Code-0.5B 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 "fiel1986/Andromeda-Code-0.5B" \
    --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": "fiel1986/Andromeda-Code-0.5B",
		"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 "fiel1986/Andromeda-Code-0.5B" \
        --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": "fiel1986/Andromeda-Code-0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fiel1986/Andromeda-Code-0.5B with Docker Model Runner:
```
docker model run hf.co/fiel1986/Andromeda-Code-0.5B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Andromeda-Code-0.5B 💻🌌

Modelo de lenguaje de 0.5B parámetros especializado en programación, destilado desde deepseek-ai/deepseek-coder-1.3b-instruct hacia Qwen/Qwen2.5-0.5B-Instruct.

DeepSeek y Qwen usan tokenizers distintos, así que la destilación por logits no aplica directamente. Acá se usó response distillation: el teacher genera respuestas de código, se filtran y limpian, y el student aprende con SFT sobre ese dataset sintético.

🎯 Método de destilación

Teacher: deepseek-ai/deepseek-coder-1.3b-instruct
Student: Qwen/Qwen2.5-0.5B-Instruct
Técnica: Response Distillation (Answer-Guided) + SFT
1. Generación de respuestas de código con el teacher
2. Limpieza: extracción de bloques ```python, remoción de texto introductorio
3. Conversión al chat template de Qwen (formato JSONL)
4. Fine-tuning del student (3 epochs, lr=2e-5, bf16)

⚠️ Estado experimental

Este modelo fue entrenado con un dataset de prueba de solo 20 ejemplos. Sirve para validar el pipeline de destilación cross-tokenizer, pero su calidad es limitada. Próximos pasos previstos: escalar el dataset a miles de prompts (HumanEval, MBPP, código curado de GitHub).

🚀 Uso rápido

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "fiel1986/Andromeda-Code-0.5B"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")

messages = [
    {"role": "system", "content": "Sos un asistente experto en programacion."},
    {"role": "user", "content": "Escribe una funcion en Python que verifique si un numero es primo"}
]
inputs = tok.apply_chat_template(messages, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=256, do_sample=True, temperature=0.6)
print(tok.decode(out[0], skip_special_tokens=True))

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Safetensors

Model size

0.5B params

Tensor type

BF16

Model tree for fiel1986/Andromeda-Code-0.5B

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Finetuned

(1073)

this model

Quantizations

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