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")

# pip install -U transformers accelerate
# 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
```

ZeryEasyCoder27B / README.md

zagy1234

Update README.md

32acfc2 verified about 2 months ago

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---
license: apache-2.0
base_model:
- Altworld/Hemmingway-1
tags:
- merge
- mergekit
- qwen
- code
- uncensored
- text-generation
library_name: transformers
---
# currently not working try [zagy1234/ZeryEasyCoder27B-GGUF](https://huggingface.co/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](https://huggingface.co/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

```python
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))

	---
	license: apache-2.0
	base_model:
	- Altworld/Hemmingway-1
	tags:
	- merge
	- mergekit
	- qwen
	- code
	- uncensored
	- text-generation
	library_name: transformers
	---
	# currently not working try [zagy1234/ZeryEasyCoder27B-GGUF](https://huggingface.co/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](https://huggingface.co/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

	```python
	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))