solar-pro-preview-instruct
Solar Pro Preview is a 22-billion-parameter instruction-tuned language model from Upstage designed to run on a single 80GB GPU. It is a pre-release version with English-only support and a 4K context window. According to published benchmarks, it performs comparably to much larger models (e.g., Llama 3.1 70B) on several tasks, particularly instruction-following and mathematical reasoning. The model uses MIT license and is openly available without gating.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | upstage |
| Parameters | 22.1B |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 44.9k |
| Likes | 458 |
| Last updated | 2024-09-20 |
| Source | upstage/solar-pro-preview-instruct |
What solar-pro-preview-instruct is
22B parameter dense transformer trained via depth up-scaling of Phi-3-medium (14B). Instruction-tuned using ChatML template. Supports text generation via standard transformers library with trust_remote_code=True. Estimated 80GB VRAM requirement for float32 inference; quantization strategies not documented. Pre-release status with limited language coverage and 4K context length; full release planned for November 2024 with expanded language support and longer context.
Run solar-pro-preview-instruct locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="upstage/solar-pro-preview-instruct")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Estimated 80GB VRAM for float32 inference (as stated in model card). Quantization (int8, fp16, AWQ) would reduce footprint but is not documented. Inference framework must support trust_remote_code=True (transformers 4.44.2+ stated in example code). Training or fine-tuning not documented; likely requires A100 80GB or H100 for reasonable throughput.
Not addressed in model card. LoRA/QLoRA feasibility unknown; 22B model on single 80GB GPU leaves <10GB margin for training. Standard SFT fine-tuning would likely require 2× 80GB+ GPUs or multi-GPU setup. Consider quantization (4-bit QLoRA) to fit on single GPU, but custom_code flag indicates non-standard architecture—test thoroughly before committing to fine-tuning pipeline.
When to avoid it — and what to weigh
- Multi-Language Requirements — Model is English-only in pre-release form. Official version planned for November 2024. Not suitable for production multi-lingual systems today.
- Long Context / Document Processing — Maximum context length is 4K tokens. Unsuitable for processing long documents, extended conversations, or RAG systems requiring >4K token windows.
- Production Systems Without Stability Validation — Pre-release label and 2024-09-20 date indicate early status. Organizations requiring stable, mature LLM deployments should validate extensively or wait for official November 2024 release.
- Fine-Tuning with Limited Hardware — 22B model on 80GB GPU leaves minimal headroom for training-time optimizations (LoRA, QLoRA). Full fine-tuning requires additional memory or multi-GPU setups.
License & commercial use
MIT license. Permissive OSI-approved license allowing free use, modification, and redistribution for any purpose (commercial, proprietary, closed-source) with attribution.
MIT license explicitly permits commercial use without restriction. No gating, no API key requirement for downloads, no commercial terms embedded. Deploy in production without legal review beyond standard MIT compliance (retain license header). No enterprise agreement or SLA documented; direct support likely through HuggingFace discussions or Upstage channels.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | High |
Model includes trust_remote_code=True flag, indicating custom Python execution during loading. Review custom code before deploying in restricted environments. No adversarial robustness, jailbreak resistance, or safety-layer documentation provided. Pre-release status may imply less rigorous security vetting. Standard LLM risks apply: prompt injection, data leakage in training, and model inversion attacks are not addressed. Assume responsibility for prompt validation and output filtering in production.
Alternatives to consider
Phi-3.5-MoE
41.9B MoE (6.6B active), MIT license, similar release timeline. Larger effective capacity but higher compute cost and more complex routing. Consider if task complexity warrants additional parameters.
Llama 3.1 8B
8B dense model, Llama license (requires review for commercial use), can fit on consumer GPUs (40GB). Smaller, simpler, but lower benchmark performance. Good for cost-sensitive or edge deployments.
Gemma 2 27B
27B dense model, Gemma license (requires review for commercial use), strong IFEval (75.36). Larger than Solar Pro, requires 80GB+ GPU, but established documentation. Consider if single-GPU constraint is flexible.
Ship solar-pro-preview-instruct with senior software developers
Our AI engineering team can help you architect single-GPU inference pipelines, fine-tuning strategies, and production monitoring for Solar Pro. Contact us to discuss your LLM deployment requirements and explore whether Solar Pro or an alternative fits your use case.
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solar-pro-preview-instruct FAQ
Can I use this model commercially in production?
What GPU do I need to run Solar Pro Preview?
What is the maximum context window?
Does the model support system prompts?
Custom software development services
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If solar-pro-preview-instruct is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy Solar Pro Preview?
Our AI engineering team can help you architect single-GPU inference pipelines, fine-tuning strategies, and production monitoring for Solar Pro. Contact us to discuss your LLM deployment requirements and explore whether Solar Pro or an alternative fits your use case.