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Ilides/coser-1.1-code-GGUF overview

license: apache 2.0 base model: Ilides/coser 1 by ilides tags: coser 1.1 code ilides coding assistant qwen3.5 lora language: en pipeline tag: text generation C…

ggufcoser-1.1-codeilidescoding-assistantqwen3.5loratext-generationenbase_model:Ilides/coser-1-by-ilidesbase_model:adapter:Ilides/coser-1-by-ilideslicense:apache-2.0endpoints_compatibleregion:usconversational

Runs locally from ~774.2 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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coser-1.1-code-f16.ggufGGUFF161.41 GBDownload
coser-1.1-code-q8_0.ggufGGUFQ8_0774.2 MBDownload

Model Details

Model IDIlides/coser-1.1-code-GGUF
AuthorIlides
Pipelinetext-generation
Licenseapache-2.0
Base modelIlides/coser-1-by-ilides
Last modified2026-07-27T20:22:33.000Z

Model README

---

license: apache-2.0

base_model: Ilides/coser-1-by-ilides

tags:

- coser-1.1-code

- ilides

- coding-assistant

- qwen3.5

- lora

language:

- en

pipeline_tag: text-generation

---

Coser 1.1-code by ilides (GGUF)

Coser 1.1-code es la evolución de Coser 1, fine-tuned con 2,000 ejemplos de código de datasets públicos (Code-Feedback, CodeAlpaca, Magicoder, CodeInstruct).

Publicado por ilides.

Versiones disponibles

| Repositorio | Formato | Uso |

|-------------|---------|-----|

| Ilides/coser-1.1-code | Safetensors (HF) | Transformers, fine-tuning |

| Ilides/coser-1.1-code-GGUF | GGUF Q8_0 / F16 | llama.cpp, LM Studio |

Stats de entrenamiento

| Métrica | Valor |

|---------|-------|

| Base | Coser 1 by ilides (Qwen3.5-0.8B) |

| Dataset | 2,000 ejemplos código |

| Método | LoRA r=16 + QLoRA 4-bit |

| Steps | 181 |

| Loss | 0.8206850051879883 |

| Token accuracy | 79.2% |

| Tiempo | None min |

| GPU | NVIDIA GeForce RTX 3050 |

Benchmark real (NVIDIA GeForce RTX 3050)

| Prompt | tok/s |

|--------|-------|

| Write a Python function that reverses a linked lis... | 17.0 |

| Write a JavaScript async function to fetch and par... | 22.0 |

| Explain what binary search is and write it in Pyth... | 21.5 |

| Write a SQL query to find duplicate emails in a us... | 19.4 |

| Fix this bug: my Python function returns None inst... | 21.8 |

| Promedio | 20.3 |

Ejemplos reales

Write a Python function that reverses a linked list.

class Node:

def __init__(self, val):

self.val = val

self.next = None

def reverse_linked_list(head):

prev = None

curr = head

while curr:

next_node = curr.next

prev.next = curr

curr.next = prev

prev = curr

curr = next_node

return prev

Write a JavaScript async function to fetch and parse JSON from an API.

async function fetchJsonAPI(url) {

// Fetch the JSON data from the API

const response = await fetch(url);

const data = await response.json();

// Handle any potential errors or exceptions

if (!data) {

console.error("Error fetching JSON from API");

return null;

}

// Process the JSON data as needed

// For example, convert to a dictionary, parse a JSON object, or store in an array

// Process the data here

// ...

return data;

}

Explain what binary search is and write it in Python.

Binary search is a search algorithm that takes an array of elements and returns the index of the target element in the array. It has the following properties:

1. It operates on the sorted elements of the array.
2. It has a time complexity of O(log n), which is very efficient.
3. It uses two pointers, one starting at the beginning of the array and the other at the end.

In Python, we can use the built-in `bisect` module to implement binary search. The `bisect.bisect_left` function returns the insertion point for a given value in a sorted list, and the `bisect.bisect_right` function returns the insertion point for a given value just after the insertion point of the previous value.

Here is a Python code snippet that demonstrates binary search using the `bisect` module:

import bisect

Given sorted array

arr = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19]

Search for a value in the arr



## Uso (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

import torch

model_id = "Ilides/coser-1.1-code"

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 1.1-code by ilides, an expert AI coding assistant."},

{"role": "user", "content": "Write a Python function to reverse a string."},

]

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=0.7)

print(tokenizer.decode(out[0], skip_special_tokens=True))


## Créditos

- Base: [Ilides/coser-1-by-ilides](https://huggingface.co/Ilides/coser-1-by-ilides)
- Datasets: Code-Feedback, python_code_instructions, CodeInstruct-20K, magicoder-python-5k
- Autor: ilides

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