Instructions to use zagy1234/ZeryEasyCoder27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zagy1234/ZeryEasyCoder27B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="zagy1234/ZeryEasyCoder27B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zagy1234/ZeryEasyCoder27B")
model = AutoModelForCausalLM.from_pretrained("zagy1234/ZeryEasyCoder27B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zagy1234/ZeryEasyCoder27B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zagy1234/ZeryEasyCoder27B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zagy1234/ZeryEasyCoder27B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/zagy1234/ZeryEasyCoder27B

SGLang

How to use zagy1234/ZeryEasyCoder27B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "zagy1234/ZeryEasyCoder27B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zagy1234/ZeryEasyCoder27B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "zagy1234/ZeryEasyCoder27B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zagy1234/ZeryEasyCoder27B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zagy1234/ZeryEasyCoder27B with Docker Model Runner:
```
docker model run hf.co/zagy1234/ZeryEasyCoder27B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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))

Downloads last month: -

Safetensors

Model size

27B params

Tensor type

BF16

Model tree for zagy1234/ZeryEasyCoder27B

Base model

Qwen/Qwen3.8-27B

Finetuned

Altworld/Hemmingway-1

Finetuned

(4)

this model

Quantizations

1 model