currently not working try zagy1234/ZeryEasyCoder27B-GGUF
🚀 ZeryEasyCoder-27B (Full Precision bfloat16)
Created by @zagy1234
ZeryEasyCoder-27B is a high-performance 27-billion parameter hybrid merge based on the Qwen architecture. It combines advanced reasoning, deep programming capabilities, and unrestricted technical utility.
💡 Looking for local inference (Ollama / GGUF)?
Check out the GGUF quantized version here: zagy1234/ZeryEasyCoder27B-GGUF
🏗️ Model Architecture & Base Models
Merged using mergekit from two state-of-the-art models:
- Hemmingway-1: Superior reasoning, natural dialogue, and unrestricted versatility.
- NEO-CODER MAX (Turbo Cold Fusion): Low-level programming expertise, script generation, and system automation.
Specifications
- Developer: zagy1234
- Parameters: 27B
- Precision: bfloat16 (Safetensors)
- Format: Hugging Face Transformers
✨ Features & Strengths
- Programming Mastery: Advanced capabilities in Python, C++, Bash/Fish, C#, and automation scripts.
- Linux & Systems Administration: Tailored for terminal workflows, system configuration, and tool integration.
- Uncensored & Unrestricted: No artificial guardrails on security research, pentesting analysis, or complex technical tasks.
💻 Usage with Hugging Face Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "zagy1234/ZeryEasyCoder27B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
prompt = "Napisz skrypt w Pythonie do automatyzacji kopii zapasowej."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))