Qwen2.5-1.5B-quantized.w8a8
Qwen2.5-1.5B-quantized.w8a8 is a compressed 1.5B-parameter language model optimized for efficient deployment. Both weights and activations are quantized to 8-bit, reducing memory usage by ~50% and doubling matrix-multiply throughput compared to the full-precision version. It maintains 99.8% of the base model's benchmark performance. Licensed under Apache 2.0, it is suitable for chat and conversational tasks on resource-constrained hardware.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | RedHatAI |
| Parameters | 1.8B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 929.8k |
| Likes | 4 |
| Last updated | 2024-12-03 |
| Source | RedHatAI/Qwen2.5-1.5B-quantized.w8a8 |
What Qwen2.5-1.5B-quantized.w8a8 is
This model is an INT8 weight and INT8 activation quantized derivative of Qwen2.5-1.5B (1.78B parameters). Quantization uses symmetric static per-channel scheme for weights and symmetric dynamic per-token scheme for activations, applied only to linear operators in transformer blocks. Released October 2024 by Neural Magic via RedHatAI. Deployment recommended via vLLM backend with OpenAI-compatible API support. Average OpenLLM benchmark score: 58.34 (vs. 58.48 unquantized, 99.8% recovery).
Run Qwen2.5-1.5B-quantized.w8a8 locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="RedHatAI/Qwen2.5-1.5B-quantized.w8a8")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 with vLLM): Full-precision 1.5B ~3 GB FP32. INT8 quantization reduces to ~1.5 GB. Single GPU (e.g., A10G, T4, RTX 3080) sufficient for inference. Multi-GPU via tensor parallelism supported by vLLM. Exact memory depends on batch size, max_model_len (example uses 8192), and KV cache strategy.
Card does not address LoRA, QLoRA, or fine-tuning feasibility for quantized weights. Query Neural Magic documentation or verify if compressed-tensors format permits training-time adapter attachment. INT8 post-training quantization typically locks weights; full-precision base model (Qwen/Qwen2.5-1.5B) may be preferred for fine-tuning.
When to avoid it — and what to weigh
- Complex Reasoning or Domain Expertise Required — 1.5B parameters and modest OpenLLM scores (58.34 avg) indicate limited performance on STEM, specialized knowledge, or multi-step reasoning tasks.
- Zero-Shot Long-Context Processing — Context length is not specified in the card. Unknown maximum sequence length may limit document analysis, summarization, or long-form generation tasks.
- Quantization Sensitivity is Critical — INT8 quantization involves lossy compression. Tasks requiring numerical precision or low-error outputs should benchmark against full-precision versions first.
- Multilingual or Low-Resource Language Specialization — Card states 'multiple languages' support without specifics. No detailed per-language performance data provided; coverage and quality unknown.
License & commercial use
Apache 2.0 license (OSI-compliant). Full license text available at base model repository. No additional restrictions, gating, or proprietary terms stated.
Apache 2.0 is a permissive OSI license. Commercial use (including closed-source products, SaaS, and for-profit deployment) is explicitly permitted with attribution. No commercial licensing restrictions or paid tier identified. Safe for production commercial use without further negotiation.
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 explicit security audit, adversarial robustness, or safety alignment details provided. Inherits base Qwen2.5 model behavior. TruthfulQA score (46.14) indicates moderate truthfulness; typical hallucination/factual error rates not quantified. Quantization does not introduce known new attack vectors but may alter model calibration and uncertainty estimates. Perform threat modeling and red-teaming appropriate to use case (e.g., customer-facing chat, sensitive data).
Alternatives to consider
Mistral-7B-Instruct-v0.2
7B parameter model with better reasoning. Requires more VRAM but offers stronger general-purpose performance if compute budget allows.
Phi-3-mini (3.8B quantized)
Comparable size with different optimization approach. Designed for efficiency; benchmark and inference cost trade-offs differ.
Qwen2.5-1.5B (full precision)
Unquantized base model. Choose if INT8 quantization causes unacceptable accuracy loss for your use case, or if hardware supports full precision.
Ship Qwen2.5-1.5B-quantized.w8a8 with senior software developers
Qwen2.5-1.5B quantized combines low resource footprint with strong conversational performance. Start with vLLM examples in the model card, benchmark on your workload, and scale with Devco's private LLM and custom app services.
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Qwen2.5-1.5B-quantized.w8a8 FAQ
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Software development & web development with DEV.co
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 Qwen2.5-1.5B-quantized.w8a8 is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy Efficient LLM Inference?
Qwen2.5-1.5B quantized combines low resource footprint with strong conversational performance. Start with vLLM examples in the model card, benchmark on your workload, and scale with Devco's private LLM and custom app services.