ReflectionCoder-CL-34B
Released by SenseLLM in 2024, ReflectionCoder-CL-34B is a 34 billion parameter chat model. ReflectionCoder-CL-34B is an open-weights chat model with roughly 34 billion parameters.
by SenseLLM · 34B parameters
Best for
Ways to use ReflectionCoder-CL-34B in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your SenseLLM API key. osFoundry discovers ReflectionCoder-CL-34B 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
ReflectionCoder-CL-34B 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 ReflectionCoder-CL-34B
ReflectionCoder-CL-34B runs on a 24GB consumer or workstation GPU (~21 GB VRAM with KV-cache headroom). Full-precision inference requires an H200 141GB or 2x A100 80GB at FP16 (~82 GB).
ReflectionCoder-CL-34B vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about ReflectionCoder-CL-34B
Is ReflectionCoder-CL-34B free to use?
ReflectionCoder-CL-34B 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 ReflectionCoder-CL-34B 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 ReflectionCoder-CL-34B need?
Approximately 21 GB at Q4 quantisation, or 82 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run ReflectionCoder-CL-34B locally?
Yes. ReflectionCoder-CL-34B is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is ReflectionCoder-CL-34B best at?
ReflectionCoder-CL-34B is well-suited to text generation.
How do I use ReflectionCoder-CL-34B in osFoundry?
Paste your SenseLLM API key in the key dialog (or deploy the open weights for self-hostable models), assign ReflectionCoder-CL-34B to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by SenseLLM on May 28, 2024. Source: https://huggingface.co/SenseLLM/ReflectionCoder-CL-34B