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:
1Instruct: Given a query, retrieve relevant passages that answer the query
2Query: {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.
