llama-q4_k_m_quantized
llama-q4_k_m_quantized is a chat model from BilalKhan1, released July 9, 2024. llama-q4_k_m_quantized is an open-weights chat model.
by BilalKhan1
Best for
- low-latency chat and routing
- request routing and triage
- text classification
Ways to use llama-q4_k_m_quantized in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your BilalKhan1 API key. osFoundry discovers llama-q4_k_m_quantized 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
llama-q4_k_m_quantized 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.
llama-q4_k_m_quantized vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about llama-q4_k_m_quantized
Is llama-q4_k_m_quantized free to use?
llama-q4_k_m_quantized 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 llama-q4_k_m_quantized commercially?
Commercial use is allowed with conditions. Licence terms not specified — verify the upstream model card before commercial use. Check upstream documentation.
Can I run llama-q4_k_m_quantized locally?
Yes. llama-q4_k_m_quantized is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is llama-q4_k_m_quantized best at?
llama-q4_k_m_quantized is well-suited to low-latency chat and routing, request routing and triage, text classification.
How do I use llama-q4_k_m_quantized in osFoundry?
Paste your BilalKhan1 API key in the key dialog (or deploy the open weights for self-hostable models), assign llama-q4_k_m_quantized to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by BilalKhan1 on July 9, 2024. Source: https://huggingface.co/BilalKhan1/llama-q4_k_m_quantized