Coser 1.1-code by ilides (HF Safetensors)

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.

```python
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:

It operates on the sorted elements of the array.
It has a time complexity of O(log n), which is very efficient.
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))