Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k
Agnuxo's Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k packs 1 billion parameters into a chat model. Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k is an open-weights chat model with roughly 1 billion parameters.
by Agnuxo · 1B parameters
Best for
Ways to use Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k in osFoundry
Connect with your own key (BYOK)
Open the key dialog and paste your Agnuxo API key. osFoundry discovers Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k 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
Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k 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 Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k
Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k runs on a single 16GB consumer GPU (~1 GB VRAM with KV-cache headroom). Full-precision inference fits on a single H100 80GB at FP16 precision (~3 GB).
Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k vs similar models
Licence
Unspecified — Licence terms not specified — verify the upstream model card before commercial use.
Check upstream documentation.
Frequently asked about Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k
Is Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k free to use?
Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k 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 Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k 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 Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k need?
Approximately 1 GB at Q4 quantisation, or 3 GB at full FP16 precision. Fits on a single 24GB consumer GPU.
Can I run Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k locally?
Yes. Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k is open-weights and runs locally on a workstation GPU. osFoundry's local runtime handles model loading, quantisation, and routing.
What is Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k best at?
Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k is well-suited to text generation.
How do I use Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k in osFoundry?
Paste your Agnuxo API key in the key dialog (or deploy the open weights for self-hostable models), assign Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k to a Maestro role in the Pipeline tab, then use it in chat, Room Apps via invokeAI, or your own apps.
Published by Agnuxo on August 31, 2024. Source: https://huggingface.co/Agnuxo/Tinytron-1B-TinyLlama-Instruct_CODE_Python-Spanish_English_GGUF_q5_k