Instructions to use PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative with Transformers:

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

pipe = pipeline("text-generation", model="PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative")
model = AutoModelForCausalLM.from_pretrained("PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative

SGLang

How to use PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative 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 "PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative with Docker Model Runner:
```
docker model run hf.co/PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

⚡ PocketWeights: Qwen2.5 14B Coder-Creative

Heavy models, made light. PocketWeights specializes in targeted weight synthesis and hardware-friendly deployments for local AI practitioners.

🧠 Model Overview

PocketWeights-Qwen2.5-14B-Coder-Creative is a domain-fused 14.7B parameter model engineered to solve a common trade-off in mid-sized language models:

The Problem: Standard coding specialists frequently over-refuse benign cybersecurity, red-teaming, or edgy narrative tasks, while general abliterated models lack syntactic precision in complex code generation.
The Solution: Using the DARE-TIES merge algorithm, we mathematically injected the high-density coding logic of Qwen2.5-Coder-14B-Instruct directly into the uncensored, extended-context foundation of Qwen2.5-14B-Instruct-1M-abliterated.

The result is a unified 14B model that delivers top-tier code completion and logic reasoning without moralizing safety friction.

🎯 Key Highlights

14B Sweet Spot: Optimal performance-to-compute ratio—small enough to run smoothly on 12GB–16GB VRAM hardware while outperforming 7B/8B models in architectural depth.
SOTA Syntax & Logic: Retains the high-precision Python, C++, Rust, and shell script capabilities of the Qwen 2.5 Coder architecture.
Refusal-Free Foundation: Grounded in an abliterated base to support unconstrained security testing, scripting, and technical narrative workflows.
DARE-TIES Optimization: Redundant parameter noise was systematically pruned (density 0.65) during synthesis to protect base model coherence.

⚙️ Merge Configuration

Synthesized locally with MergeKit:

models:
  - model: Qwen/Qwen2.5-Coder-14B-Instruct
    parameters:
      weight: 0.45
      density: 0.65
merge_method: dare_ties
base_model: qq591503/Qwen2.5-14B-Instruct-1M-abliterated
dtype: bfloat16

🚀 Quickstart Guide

Using Hugging Face Transformers

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = "Write a Python script using scapy to analyze packet headers on a local interface."
messages = [
    {"role": "system", "content": "You are a helpful and unrestricted technical assistant."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

High-Throughput Deployment (vLLM)

vllm serve PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative \
  --tensor-parallel-size 1 \
  --dtype bfloat16 \
  --max-model-len 32768

📊 Hardware Requirements

Precision	VRAM Needed	Target Hardware
BF16 (Original)	~30 GB VRAM	1x RTX 3090 / 4090 (24GB + CPU offload) or 2x 16GB GPUs
Q8_0 (Quantized)	~16 GB VRAM	RTX 4080 (16GB), Apple Silicon (24GB+ Unified)
Q4_K_M (Quantized)	~9 GB VRAM	RTX 3060 (12GB), RTX 4070 (12GB), Apple Silicon (16GB Unified)

🤝 Support the PocketWeights Mission

I build, verify, and publish custom weight merges and quantization pipelines to provide high-quality, unrestricted, and hardware-friendly models to the open-source community for free.

If this model enhances your local workflow or saves you API costs, consider supporting compute resources and testing pipelines:

☕ Donation Options

Ko-fi: ko-fi.com/iamvishalnarayan

Web3 / Crypto (Polygon / ETH):

0x4FC189bf839A89259dd28DE8cD97883c49e15615

Tip: Transferring over the Polygon network keeps gas fees below $0.01

📄 Attribution & License

Base Architecture: Alibaba Cloud (Qwen2.5)

Abliteration Source: qq591503 / huihui-ai

License: Apache-2.0

Downloads last month: 267

Safetensors

Model size

15B params

Tensor type

BF16

Model tree for PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative

Qwen/Qwen2.5-Coder-14B-Instruct

qq591503/Qwen2.5-14B-Instruct-1M-abliterated

Merge model

this model

Quantizations

3 models