Qwen3.5-Embedding-0.8B

An embedding model fine-tuned from Qwen/Qwen3.5-0.8B-Base for agent memory retrieval and related domain-specific semantic retrieval.

Model Overview

• Model Type: Text Embedding
• Supported Languages: Chinese, English
• Number of Parameters: 752M (752,393,024)
• Context Length: 2048 tokens
• Embedding Dimension: 1024 (supports MRL dimensions 128, 256, 512, 768, 1024)

Model Details

Property	Value
Base model	Qwen/Qwen3.5-0.8B-Base
Architecture	Qwen3.5 hybrid text model
Layers	24 (18 linear-attention + 6 full-attention)
Hidden size	1024
Intermediate size	3584
Vocabulary size	248,320
Embedding output	1024 dimensions
Pooling	Last-token pooling
Normalization	L2 normalization
Similarity	Cosine similarity or normalized dot product
Training precision	BF16
MRL dimensions	128, 256, 512, 768, 1024
License	Apache-2.0

Intended Use

Chinese/English semantic retrieval for agent memory, personal knowledge bases, conversation archives, technical documentation, and related domain-specific search, including short keyword queries and natural questions.

Encoding Protocol

Queries:

Instruct: Given a query, retrieve relevant passages that answer the query
Query: {query}

Memories/passages: no instruction prefix.

Use last-token pooling and L2 normalization. If using Matryoshka truncation, truncate to 128, 256, 512, or 768 dimensions before the final L2 normalization.

This is not a sentence-transformers package with a built-in pooling wrapper; implement the pooling and query prefix as described above.

Comparison with Qwen/Qwen3-Embedding-0.6B

S2 is this model; Official is Qwen/Qwen3-Embedding-0.6B. Values are Recall@K or MRR. The same tokenizer protocol, query instruction, pooling, normalization, corpus, and query set were used per row.

OpenClaw fine-tuning validation

Dim	S2 R@1	Official R@1	S2 R@3	Official R@3	S2 R@5	Official R@5	S2 R@10	Official R@10	S2 MRR	Official MRR
128	8.96%	17.19%	19.57%	31.84%	25.82%	37.98%	35.32%	46.49%	17.57%	27.38%
256	15.04%	20.12%	30.74%	35.99%	38.58%	43.67%	50.14%	52.35%	26.46%	31.25%
512	21.67%	22.33%	40.57%	39.19%	49.75%	47.21%	62.02%	57.16%	34.84%	34.11%
768	23.88%	22.94%	44.67%	40.57%	53.45%	48.31%	65.06%	58.71%	37.60%	34.97%
1024	25.21%	23.66%	45.55%	41.46%	54.62%	49.36%	66.56%	59.92%	38.86%	35.66%

Unseen memory

Corpus of 382 memory chunks dated after 2026-08-08; 1,048 Gemini-generated queries with multi-positive qrels where applicable.

Dim	S2 R@1	Official R@1	S2 R@3	Official R@3	S2 R@5	Official R@5	S2 R@10	Official R@10	S2 MRR	Official MRR
128	12.02%	18.80%	26.15%	40.55%	35.50%	49.43%	47.81%	59.73%	23.76%	33.14%
256	17.08%	20.32%	35.40%	43.89%	45.99%	51.91%	61.83%	62.02%	31.00%	35.07%
512	20.42%	23.09%	43.23%	45.32%	54.87%	54.10%	68.61%	63.93%	36.48%	37.40%
768	21.56%	22.04%	45.80%	45.80%	59.06%	54.39%	72.23%	63.84%	38.22%	36.91%
1024	22.33%	21.09%	46.47%	45.52%	58.59%	53.91%	72.04%	64.31%	38.80%	36.25%

Conventional content validation sample

Stratified sample of 382 queries from the pretraining validation split, with 761 unique positive/negative passages.

Dim	S2 R@1	Official R@1	S2 R@3	Official R@3	S2 R@5	Official R@5	S2 R@10	Official R@10	S2 MRR	Official MRR
128	80.37%	81.41%	93.46%	93.19%	96.60%	96.34%	98.43%	97.91%	87.45%	87.93%
256	87.70%	85.86%	96.60%	93.98%	98.43%	96.34%	99.74%	97.64%	92.42%	90.43%
512	89.53%	85.34%	97.91%	93.72%	99.21%	96.34%	99.74%	98.43%	93.71%	90.22%
768	91.10%	84.82%	97.64%	95.03%	99.21%	97.38%	99.74%	98.69%	94.58%	90.34%
1024	91.36%	84.29%	97.64%	95.29%	98.95%	97.12%	99.74%	98.69%	94.74%	90.13%

Recommendations

• OpenClaw's default memory search returns up to 10 results, so Recall@10 is the primary metric.
• Use 1024 dimensions for maximum ranking quality, especially when top-1/top-3 and MRR matter.
• Use 768 dimensions as the storage/Recall@5-10 compromise; it uses about 25% less vector storage than 1024 dimensions.

Base Model and License

Derived from Qwen/Qwen3.5-0.8B-Base and distributed under the base model's Apache-2.0 license. Please review the base model license and usage requirements.