Qwen2-7B-Instruct-deccp-GGUF
QuantFactory's Qwen2-7B-Instruct-deccp-GGUF packs 7 billion parameters into a chat model. Qwen2-7B-Instruct-deccp-GGUF is an open-weights chat model with roughly 7 billion parameters.
by QuantFactory · 7B parameters
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Ways to use Qwen2-7B-Instruct-deccp-GGUF in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your QuantFactory API key. osFoundry discovers Qwen2-7B-Instruct-deccp-GGUF automatically — assign it to a Maestro role (router, direct, orchestrator, or fallback) in the Pipeline tab and it is live in every chat. Your key, your provider account — no token markup.
Deploy a dedicated endpoint
Qwen2-7B-Instruct-deccp-GGUF is open-weights — run it locally for free, or deploy a dedicated GPU endpoint in your workspace for reserved capacity with no rate limits.
Use it in a Room App
Room Apps declare AI features in their manifest, then call them with invokeAI:
import { invokeAI } from '@osfoundry/app-sdk'
// 'summarize' is an AI feature declared in your app manifest.
const result = await invokeAI('summarize', userText)
Call it from your own apps
Once a model is wired into your workspace you can host it as an API and reach it from your own services, scripts, or CI — outside osFoundry.
What hardware can run Qwen2-7B-Instruct-deccp-GGUF
Qwen2-7B-Instruct-deccp-GGUF runs on a single 16GB consumer GPU (~5 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~17 GB).
Qwen2-7B-Instruct-deccp-GGUF vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Qwen2-7B-Instruct-deccp-GGUF
Is Qwen2-7B-Instruct-deccp-GGUF free to use?
Qwen2-7B-Instruct-deccp-GGUF is free to run locally on your own hardware. Hosted access through osFoundry is metered (input Free (local), output Free (local)). You can switch between local and hosted at any time.
Can I use Qwen2-7B-Instruct-deccp-GGUF commercially?
Commercial use is allowed with conditions. Licence terms not specified — verify the upstream model card before commercial use. Check upstream documentation.
How much VRAM does Qwen2-7B-Instruct-deccp-GGUF need?
Approximately 5 GB at Q4 quantisation, or 17 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run Qwen2-7B-Instruct-deccp-GGUF locally?
Yes. Qwen2-7B-Instruct-deccp-GGUF is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Qwen2-7B-Instruct-deccp-GGUF best at?
Qwen2-7B-Instruct-deccp-GGUF is well-suited to text generation.
How do I use Qwen2-7B-Instruct-deccp-GGUF in osFoundry?
Paste your QuantFactory API key in the key dialog (or deploy the open weights for self-hostable models), assign Qwen2-7B-Instruct-deccp-GGUF to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by QuantFactory on June 12, 2024. Source: https://huggingface.co/QuantFactory/Qwen2-7B-Instruct-deccp-GGUF