PocketWeights-Qwen2.5-14B-Coder-Creative

⚡ PocketWeights: Qwen2.5 14B Coder-Creative

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

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🧠 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.

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🎯 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.

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⚙️ 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

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🚀 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

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📊 Hardware Requirements

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🤝 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

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📄 Attribution & License

Base Architecture: Alibaba Cloud (Qwen2.5)

Abliteration Source: qq591503 / huihui-ai

License: Apache-2.0