VibeThinker-3B-GGUF
VibeThinker-3B is a 3-billion-parameter language model optimized for reasoning tasks—mathematics, coding, and STEM. It is offered in GGUF format (quantized for efficient local inference) under the MIT license. The model claims competitive performance on reasoning benchmarks despite its small size, making it suitable for self-hosted or edge deployments where resource constraints are tight.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | prithivMLmods |
| Parameters | Unknown |
| Context window | Unknown |
| License | mit — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 56.9k |
| Likes | 68 |
| Last updated | 2026-06-16 |
| Source | prithivMLmods/VibeThinker-3B-GGUF |
What VibeThinker-3B-GGUF is
VibeThinker-3B is derived from Qwen2.5-Coder-3B and trained using the Spectrum-to-Signal Principle (SSP) post-training pipeline, which combines curriculum SFT, multi-domain reinforcement learning (MGPO), offline self-distillation, and instruct RL. The model is provided in 15 GGUF quantizations ranging from Q2_K (1.27 GB) to F32 (12.3 GB). Context length and parameter count details are not explicitly stated in the card, though it is described as 3-billion-parameter. Supports up to 102K output tokens and is recommended for inference via vLLM or SGLang with temperature 1.0, top_p 0.95.
Run VibeThinker-3B-GGUF locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="prithivMLmods/VibeThinker-3B-GGUF")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
ESTIMATE (verify before deployment): Q2_K quantization (~1.27 GB) requires ~3–4 GB VRAM on GPU or CPU with adequate RAM. Q4_K_M (~1.93 GB) requires ~4–6 GB VRAM. BF16/F16 (~6.18 GB) requires ~10–14 GB VRAM. Full F32 (~12.3 GB) requires 16+ GB VRAM. Inference is optimized for llama.cpp (CPU/GPU acceleration) and vLLM/SGLang (GPU). Actual memory usage depends on batch size, context length, and output token count (up to 102K).
Unknown. Model card does not discuss LoRA, QLoRA, or fine-tuning feasibility. Recommend reviewing base model (WeiboAI/VibeThinker-3B) documentation or contacting prithivMLmods for guidance on supervised fine-tuning or parameter-efficient adaptation.
When to avoid it — and what to weigh
- Broad general-knowledge tasks — Model card explicitly states larger general-purpose models remain preferable for open-domain knowledge. Not optimized for trivia, historical facts, or encyclopedic recall.
- Real-time, ultra-low-latency inference — Supports up to 102K output tokens; inference time at scale is not documented. Verify latency requirements against actual deployment benchmarks.
- Non-verifiable or subjective reasoning — Model is purpose-built for tasks with clear verification signals (math, code, STEM). Performance on opinion-based, narrative, or ambiguous tasks is Unknown.
- Production use without validation — Card does not document error rates, hallucination rates, or failure modes. Requires internal testing before deployment in critical systems.
License & commercial use
MIT license. Permissive, OSI-approved open-source license allowing use, modification, and distribution with minimal restrictions (attribution required).
MIT is a permissive OSI license that explicitly permits commercial use, modification, and redistribution. No gated restrictions. You may use VibeThinker-3B-GGUF in commercial products provided you include the MIT license notice. No warranties or liability assumptions are implied by the license; you bear operational and quality risk.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Clear |
| Deployment complexity | Low |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security posture, vulnerability disclosure policy, or input sanitization details are documented. Standard LLM considerations apply: models trained on diverse internet data may encode biased, harmful, or outdated information. Output should be validated before use in safety-critical or user-facing contexts. No supply-chain attestation or model provenance verification mechanism is described; verify artifact integrity independently. Quantization process (by prithivMLmods) differs from original model; validate numerics match expectations.
Alternatives to consider
DeepSeek-Coder-3B (or similar Qwen-derived base models)
Alternative 3B models also optimized for code; may have different training recipes and community support. Compare benchmark results and inference cost.
Llama 2 7B / Llama 3 7B-Instruct
Larger but still compact; better for general-purpose reasoning and broader domain knowledge. Trade-off: more VRAM, slower inference, better conversational ability.
OpenAI API (gpt-4o mini) or Claude 3 Haiku
Cloud-hosted reasoning models with commercial SLAs. No self-hosting overhead, stronger multi-domain capabilities. Trade-off: API costs, data residency, latency, vendor lock-in.
Ship VibeThinker-3B-GGUF with senior software developers
VibeThinker-3B-GGUF enables self-hosted math and coding inference on modest hardware. Download GGUF quantizations (1.27–12.3 GB), integrate with llama.cpp or vLLM, and start reasoning. MIT license permits commercial use. Review security and validation requirements for production.
Talk to DEV.coRelated open-source tools
Surfaced by semantic similarity across the DEV.co open-source index.
Related on DEV.co
Explore the category and the services that help you build with it.
VibeThinker-3B-GGUF FAQ
Can I use VibeThinker-3B in a commercial product?
What GPU/CPU do I need to run this locally?
Is this model suitable for general chatbot applications?
How do I fine-tune or adapt this model?
Custom software development services
Adopting VibeThinker-3B-GGUF is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate open-source llms software in production.
Ready to deploy a compact reasoning model?
VibeThinker-3B-GGUF enables self-hosted math and coding inference on modest hardware. Download GGUF quantizations (1.27–12.3 GB), integrate with llama.cpp or vLLM, and start reasoning. MIT license permits commercial use. Review security and validation requirements for production.