Ilides/coser-1.3-coder

Coser 1.3-coder by ilides (HF Safetensors)

Coser 1.3-coder es el asistente de código agéntico de ilides: identidad Coser clara, tono natural (con humor ligero), y enfoque en ingeniería real — planificar, depurar y escribir código de producción.

Evolución de Coser 1.1-code, fine-tuned con 74 ejemplos curados (código, identidad, chat y correcciones de comportamiento).

Publicado por ilides.

Versiones

Repositorio	Formato	Uso
Ilides/coser-1.3-coder	Safetensors	Transformers, fine-tuning
Ilides/coser-1.3-coder-GGUF	GGUF F16 + Q8_0	llama.cpp, LM Studio

Identidad

System prompt recomendado:

You are Coser 1.3-coder by ilides, an expert AI coding assistant. Always speak in first person. Never say the user is Coser. Use natural prose unless the user explicitly asks for JSON.

Stats de entrenamiento

Métrica	Valor
Base	Coser 1.1-code (Qwen3.5-0.8B)
Dataset	74 ejemplos curados
Método	LoRA r=16 + QLoRA 4-bit
Steps	95
Épocas	5
Loss final	0.5570954799652099
Token accuracy	89.5%
Tiempo	13.7 min
GPU	NVIDIA GeForce RTX 3050

Benchmark (NVIDIA GeForce RTX 3050)

Prompt	tok/s
Write a Python function that reverses a linked l...	18.9
Write a JavaScript async function to fetch and p...	19.9
Explain what binary search is and write it in Py...	16.2
Write a SQL query to find duplicate emails in a ...	16.3
Fix this bug: my Python function returns None in...	16.2
Promedio	17.5

Uso (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Ilides/coser-1.3-coder"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, device_map="auto", trust_remote_code=True, torch_dtype=torch.bfloat16
)
messages = [
    {"role": "system", "content": "You are Coser 1.3-coder by ilides, an expert AI coding assistant."},
    {"role": "user", "content": "Who are you?"},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, temperature=0.55, repetition_penalty=1.12)
print(tokenizer.decode(out[0], skip_special_tokens=True))

GGUF (llama.cpp)

llama-cli -m coser-1.3-coder-q8_0.gguf -cnv -ngl 99

Créditos

• Base: Ilides/coser-1.1-code
• Autor: ilides