jamph-qwen2.5-coder-7b-instruct-q8_0
Released by pererikgronvik in 2026, jamph-qwen2.5-coder-7b-instruct-q8_0 is a 7 billion parameter chat model. jamph-qwen2.5-coder-7b-instruct-q8_0 is an open-weights chat model with roughly 7 billion parameters.
by pererikgronvik · 7B parameters
Best for
Ways to use jamph-qwen2.5-coder-7b-instruct-q8_0 in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your pererikgronvik API key. osFoundry discovers jamph-qwen2.5-coder-7b-instruct-q8_0 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
jamph-qwen2.5-coder-7b-instruct-q8_0 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 jamph-qwen2.5-coder-7b-instruct-q8_0
jamph-qwen2.5-coder-7b-instruct-q8_0 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).
jamph-qwen2.5-coder-7b-instruct-q8_0 vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about jamph-qwen2.5-coder-7b-instruct-q8_0
Is jamph-qwen2.5-coder-7b-instruct-q8_0 free to use?
jamph-qwen2.5-coder-7b-instruct-q8_0 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 jamph-qwen2.5-coder-7b-instruct-q8_0 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 jamph-qwen2.5-coder-7b-instruct-q8_0 need?
Approximately 5 GB at Q4 quantisation, or 17 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run jamph-qwen2.5-coder-7b-instruct-q8_0 locally?
Yes. jamph-qwen2.5-coder-7b-instruct-q8_0 is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is jamph-qwen2.5-coder-7b-instruct-q8_0 best at?
jamph-qwen2.5-coder-7b-instruct-q8_0 is well-suited to text generation.
How do I use jamph-qwen2.5-coder-7b-instruct-q8_0 in osFoundry?
Paste your pererikgronvik API key in the key dialog (or deploy the open weights for self-hostable models), assign jamph-qwen2.5-coder-7b-instruct-q8_0 to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by pererikgronvik on May 8, 2026. Source: https://huggingface.co/pererikgronvik/jamph-qwen2.5-coder-7b-instruct-q8_0