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Ilides/coser-1.3-coder
coser-1.3-coder is a text generation model from Ilides. Use it when you need the model to write or continue text. The card lists the license as apache-2.0.
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.
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.safetensors1.5 GB · 99%
From the Hugging Face model README
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.
| 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 |
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.
| 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 |
| 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 |
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))
llama-cli -m coser-1.3-coder-q8_0.gguf -cnv -ngl 99