Qwen2.5-14B-Instruct-GPTQ-Int4
Qwen2.5-14B-Instruct-GPTQ-Int4 is a 14.7-billion-parameter instruction-tuned language model from Alibaba's Qwen team, compressed to 4-bit GPTQ quantization for efficient deployment. It supports context windows up to 128K tokens, handles 29+ languages, and excels at coding, mathematics, long-form text generation, and structured output (JSON). The GPTQ quantization reduces memory footprint significantly while trading off some precision; suitable for edge/on-prem deployments with modest GPUs.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | Qwen |
| Parameters | 14.8B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 111.3k |
| Likes | 26 |
| Last updated | 2024-10-09 |
| Source | Qwen/Qwen2.5-14B-Instruct-GPTQ-Int4 |
What Qwen2.5-14B-Instruct-GPTQ-Int4 is
Architecture: Transformer with RoPE, SwiGLU, RMSNorm, GQA (40 Q-heads, 8 KV-heads), 48 layers, 13.1B non-embedding parameters. Quantization: GPTQ 4-bit. Context: Full 131,072 tokens (input), 8,192 token generation. Training: Pretraining + post-training (instruction-tuned). Requires transformers≥4.37.0. YaRN rope-scaling for sequences >32K tokens (caveat: vLLM static YaRN may degrade performance on short contexts). Not gated; Apache-2.0 licensed.
Run Qwen2.5-14B-Instruct-GPTQ-Int4 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="Qwen/Qwen2.5-14B-Instruct-GPTQ-Int4")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: ~7–10 GB VRAM (4-bit GPTQ, int4 weights + activations on single GPU). Baseline: NVIDIA A10, A100 (40GB), or RTX 4090 feasible for single-instance serving. For batching/production throughput, 24–80GB GPUs (H100, A100) recommended. See Qwen docs for official speed benchmarks. Requires CUDA/ROCm and transformers library.
Card does not explicitly discuss fine-tuning feasibility. GPTQ quantization typically supports LoRA/QLoRA (parameter-efficient adaptation) but adds complexity (requires auto_gptq and special configurations). Full fine-tuning on 4-bit is non-standard. Recommendation: consult official Qwen docs for quantized LoRA support; if not available, consider unquantized variant for fine-tuning or use instruction-tuned base as-is.
When to avoid it — and what to weigh
- Highest accuracy required without precision loss — 4-bit quantization introduces information loss compared to full-precision (bfloat16) models. For tasks demanding maximum precision (e.g., specialized reasoning), benchmark against full-precision variant first.
- Very short context exclusively — YaRN rope-scaling (needed for >128K contexts) is static in vLLM and may degrade performance on short texts. If only short contexts are used, consider full-precision or configure YaRN carefully.
- Real-time inference with strict sub-100ms latency — 14B parameters still require non-trivial compute. Verify throughput/latency against your hardware (GPU VRAM, bandwidth) before committing to real-time SLA.
- Production without internal testing — No explicit security audit, adversarial robustness, or bias assessment disclosed. Requires internal evaluation before high-stakes deployment (content moderation, legal, medical).
License & commercial use
Apache-2.0 (OSI-approved permissive license). Permits commercial use, modification, and distribution with minimal restrictions.
Apache-2.0 is a clear OSI-approved open-source license permitting commercial use. No gating, no commercial restrictions stated. However, commercial deployments should: (1) include Apache-2.0 license attribution, (2) verify model outputs meet your compliance needs (no audit of bias/safety disclosed), (3) review Alibaba's usage terms if any service TOS applies. No trademark/branding restrictions inferred from license alone.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
No security audit, adversarial robustness testing, or vulnerability assessment disclosed. GPTQ quantization does not inherently improve or worsen security posture. Model is instruction-tuned but no jailbreak/prompt-injection mitigations documented. Recommendations: (1) validate outputs on your use case before production, (2) implement input filtering if user-facing, (3) monitor for unintended outputs (hallucinations, harmful content), (4) use in isolated/sandboxed environments for untrusted inputs.
Alternatives to consider
Qwen2.5-14B-Instruct (full precision bfloat16)
Same model, no quantization loss; larger VRAM footprint (~28–35 GB); higher accuracy if precision is critical.
Meta Llama 3.1 8B (various quantization)
Smaller footprint, strong open-source ecosystem, but fewer languages and less recent. Consider if memory is extremely constrained.
Mistral 7B or 8x7B Mixture-of-Experts
Smaller/efficient alternatives with good code and reasoning capability; less multilingual support. Trade-off: lower capability for lower cost.
Ship Qwen2.5-14B-Instruct-GPTQ-Int4 with senior software developers
Start with vLLM or TGI, verify hardware (7–10 GB VRAM minimum), and test the quickstart code on your infra. Review official Qwen benchmarks and documentation for production tuning.
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Qwen2.5-14B-Instruct-GPTQ-Int4 FAQ
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Software developers & web developers for hire
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 Qwen2.5-14B-Instruct-GPTQ-Int4 is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy Qwen2.5-14B Locally?
Start with vLLM or TGI, verify hardware (7–10 GB VRAM minimum), and test the quickstart code on your infra. Review official Qwen benchmarks and documentation for production tuning.