57 lines
1.3 KiB
Markdown
57 lines
1.3 KiB
Markdown
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---
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license: apache-2.0
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base_model: Qwen/Qwen3-14B
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tags:
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- quantized
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- 4-bit
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- int4
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- qwen3
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language:
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- en
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pipeline_tag: text-generation
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---
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# Qwen3-14B-AWQ-INT4
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INT4 quantization of [`Qwen/Qwen3-14B`](https://huggingface.co/Qwen/Qwen3-14B). Built to run on a single consumer GPU (≥10 GB VRAM).
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## Footprint
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| Source params | 14B |
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| Quantized weights | ~9.4 GB on disk |
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| Inference VRAM (incl. KV cache @ 32K context) | ~16 GB |
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Fits any 16 GB+ consumer card. No homelab needed.
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## Bench
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Scored on [`drawais/needle-1M-bench-mvp`](https://huggingface.co/datasets/drawais/needle-1M-bench-mvp) (50K-token haystack, real arxiv text):
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| Metric | Score |
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|---|---|
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| Overall recall | **90.0%** |
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| Paper-anchored | 80.0% |
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| Synthetic codes | 100.0% |
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## Quick start
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```bash
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vllm serve drawais/Qwen3-14B-AWQ-INT4 --quantization awq_marlin --max-model-len 32768
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```
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tok = AutoTokenizer.from_pretrained("drawais/Qwen3-14B-AWQ-INT4")
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model = AutoModelForCausalLM.from_pretrained("drawais/Qwen3-14B-AWQ-INT4", device_map="auto")
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```
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## Context length
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Native: 40,960 tokens (inherits from base model). For longer contexts, enable YaRN rope-scaling per the base model's config — supported by vLLM and Transformers.
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## License
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Apache 2.0 (inherits from base model).
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