base_model: Qwen/Qwen3-Embedding-4B
base_model_relation: quantized
library_name: transformers
tags:
- fp8
- compressed-tensors
- llm-compressor
- vllm
- embedding
Qwen3-Embedding-4B-FP8-Dynamic
Qwen/Qwen3-Embedding-4B quantised to FP8 with llm-compressor, for serving with vLLM.
What was done
| Scheme | FP8_DYNAMIC — weights static per-channel FP8, activations dynamic per-token FP8 |
| Calibration | none needed; dynamic activation scales are computed at inference time |
| Left in bf16 | lm_head, and the token embeddings |
| Weights before | 7.49 GiB |
| Weights after | 4.11 GiB (45% smaller) |
Embeddings are the pooled last hidden state; the head is unused and left unquantised. Cosine similarity is scale-sensitive, so compare rankings rather than raw distances when validating this checkpoint.
Quality gate
WikipediaRetrievalMultilingual from MTEB — retrieval over Wikipedia in 16 languages, scored by ndcg_at_10.
| language | bf16 | FP8 | Δ |
|---|---|---|---|
| de | 0.9066 | 0.9065 | -0.0001 |
| en | 0.9344 | 0.9353 | +0.0009 |
| it | 0.8993 | 0.8994 | +0.0002 |
| mean | 0.9134 | 0.9138 | +0.0003 |
Why
Serving Qwen/Qwen3-Embedding-4B in bf16 leaves little room for KV cache on a small GPU: the weights take what the cache needs, and the context length has to be cut until it fits. Halving the weights gives that memory back — the same card serves a longer context without any other change.
Serving
vllm serve DCC-BS/Qwen3-Embedding-4B-FP8-Dynamic \
--task embed \
--max-model-len 32768 \
--gpu-memory-utilization 0.85
The checkpoint is in compressed-tensors format, so vLLM detects the quantisation from
config.json; no extra flag is required.