Beta

Explore
Marketplace
Neural Labs
Chat
Wallet
Docs

Beta

Explore Marketplace Neural Labs Wallet Docs

Build

Manage

My assets Manage wallet Organization

Community

Discord X (Twitter)Telegram

Docs Privacy Policy Terms and Conditions

Proud member of

Proud member of

Docs Cookie Policy

Privacy Policy Terms and Conditions

© 2026 AlphaNeural AI. All rights reserved.

cyberspace-qwen25-7b-lora – AI Model by JupiterJil | AlphaNeural AI

You can deploy this model and start earning money today!

JupiterJil/
cyberspace-qwen25-7b-lora

like

0

Qwen/Qwen2.5-7B-Instruct

Views

No views yet

Cyberspace Presales Co-Pilot — Qwen 2.5 7B LoRA Adapter

LoRA adapter for Cyberspace Limited's Vertical AI Presales Co-Pilot.

Training Summary

Base model: Qwen/Qwen2.5-7B-Instruct
Method: LoRA (QLoRA 4-bit) via Unsloth
Dataset: 132 Cyberspace proposals (ChatML format, zero template bleed)
Final loss: 1.4795 (target: 1.2–1.6 ✅)
Training time: ~4.9 min on A100 80GB

LoRA Config

Rank: 16
Alpha: 16
Dropout: 0.05
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Epochs: 3
LR: 2e-4 (cosine scheduler)
train_on_responses_only: True (ChatML markers)

Usage

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = PeftModel.from_pretrained(base, "JupiterJil/cyberspace-qwen25-7b-lora")

Recommended Inference Settings

Temperature: 0.4
Top-p: 0.9
Repeat penalty: 1.1
Use detailed category-specific system prompts

Production Artifact

For inference, use the GGUF Q4_K_M version: JupiterJil/cyberspace-qwen25-7b-gguf