---
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
library_name: peft
tags:
  - memory-encoder
  - lora
  - structured-extraction
  - lycheemem
language:
  - en
pipeline_tag: text-generation
---

# Encoder v0 — Memory Encoder for LycheeMem

A LoRA adapter on top of **Qwen2.5-0.5B-Instruct** that turns conversation turns into structured `MemoryRecord` JSON (typed, atomic, with entities / temporal / evidence span / source_role). Trained by distilling DeepSeek V4 Flash and selecting high-quality candidates via a 4-dim verifier.

Designed as a drop-in encoder for [LycheeMem](https://github.com/LycheeMem/lycheemem)'s write-side memory pipeline, with **physical JSON schema guarantee via constrained decoding** (outlines + Pydantic).

## Highlights

- **8.7 MB LoRA adapter** on a 0.5B base — runs locally on a single RTX 4060 Ti 8GB, zero API cost
- **+125% weighted_score** over the runtime Qwen2.5-7B baseline on a 519-sample held set
- **100% JSON schema compliance** with constrained decoding (vs 74% for runtime baseline, 96-98% for SOTA prompt-only)
- **4× faster** than the runtime baseline (3.4s vs 20s p50 latency)
- On LongMemEval-style task dialogs, **outperforms even Qwen2.5-72B and V4 Flash teacher** on weighted_score (3.749 vs 3.666 / 3.700)

## Evaluation

Evaluated on 519 held-out conversation segments (LongMemEval-S + MSC-MemFuse-MC10, English personal dialogs). The **weighted_score** is a 4-dim LLM-as-judge metric (V4 Flash) on `atomicity / self_containedness / entity_coverage / evidence_alignment`, weighted 0.25 / 0.30 / 0.20 / 0.25, with failures scored 0 (out of 5.0).

### 7-Model Leaderboard

| rank | model | size | weighted_score | schema_ok | latency p50 |
|---|---|---|---|---|---|
| 1 | DeepSeek-V3 | 671B (MoE) | 4.057 | 96.9% | 44s |
| 2 | Qwen2.5-72B-Instruct | 72B | 3.951 | 98.8% | 33s |
| 3 | DeepSeek V4 Flash (teacher) | — | 3.833 | 95.8% | 14s |
| **4** | **encoder_v0 (this model)** | **0.5B + LoRA** | **3.775** | **100.0%** | **3.4s** |
| 5 | Qwen3-32B | 32B | 3.476 | 97.7% | 67s |
| 6 | Qwen2.5-14B-Instruct | 14B | 1.946 | 80.5% | 19s |
| 7 | Qwen2.5-7B-Instruct (runtime baseline) | 7B | 1.679 | 74.0% | 20s |

### 4-Dim Quality Breakdown

| model | atomicity | self_cont | entity_cov | evidence |
|---|---|---|---|---|
| DeepSeek-V3 | 4.61 | 4.90 | 4.27 | 3.60 |
| Qwen2.5-72B | 4.89 | 4.85 | 4.14 | 3.54 |
| V4 Flash (teacher) | 4.48 | 4.88 | 4.21 | 3.94 |
| **encoder_v0** | **4.53** | **4.51** | **2.93** ⚠️ | **3.30** |
| Qwen3-32B | 4.38 | 4.74 | 4.13 | 3.18 |
| Qwen2.5-7B | 4.20 | 4.47 | 3.27 | 2.98 |

`entity_coverage` is the model's main known weakness (1.0-1.3 points below SOTA), planned to be addressed in v2.

### Per-Source Breakdown

| model | LongMemEval | MSC |
|---|---|---|
| DeepSeek-V3 | 3.871 | 4.357 |
| Qwen2.5-72B | 3.666 | 4.408 |
| V4 Flash (teacher) | 3.700 | 4.047 |
| **encoder_v0** | **3.749** | **3.817** |
| Qwen2.5-7B (baseline) | 1.330 | 2.241 |

On task-oriented dialogs (LongMemEval), encoder_v0 actually **surpasses both Qwen2.5-72B and the V4 Flash teacher**.

## Training

```text
Pipeline:
  Stage 1: 5000 conversation segments from LongMemEval-S + MSC-MemFuse-MC10
  Stage 2a: V4 Flash distillation → 4769 candidate record sets
  Stage 2b: Rule + V4 Flash verifier (4-dim ≥ 4.0) → 2590 pseudo-gold
  Stage 2c: +394 synthetic advice-class samples (gold = empty records)
  Stage 3:  LoRA SFT on Qwen2.5-0.5B-Instruct
            rank=16, alpha=32, dropout=0.05
            target_modules = q_proj, k_proj, v_proj, o_proj
            3 epochs, batch=1*accum16, lr=2e-4, bf16
            28.5 min on RTX 4060 Ti 8GB

Trainable params: 2.16M / 496M = 0.44%
Final eval loss: 0.293
```

Total training cost: ~¥24 (API for distillation + verifier) + 28 min local GPU.

## Intended Use

**Primary use**: Drop-in write-side encoder for LycheeMem (or similar long-term memory systems) that takes a conversation segment and outputs `MemoryRecord` JSON suitable for storage and downstream retrieval.

**Input format**:

```python
{
  "previous_turns": [{"role": "user", "content": "..."}, ...],  # optional
  "current_turns": [{"role": "user", "content": "..."}, ...],   # required
  "session_date": "2026-05-12"  # optional, ISO or freeform
}
```

**Output format** (strict JSON, guaranteed by constrained decoding):

```python
{
  "records": [
    {
      "memory_type": "fact|preference|event|constraint|procedure|failure_pattern|tool_affordance",
      "semantic_text": "User plans to visit Beijing on 2026-05-20 to meet Li Hua.",
      "entities": ["Beijing", "Li Hua"],
      "temporal": {"t_ref": "2026-05-12", "t_valid_from": "2026-05-20", "t_valid_to": ""},
      "tags": ["travel", "meeting"],
      "evidence_turns": [0],
      "source_role": "user"
    }
  ]
}
```

## How to Use

### Install dependencies

```bash
pip install transformers peft outlines pydantic torch
```

### Inference (with constrained decoding — recommended)

```python
import torch
import outlines
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
from pydantic import BaseModel
from typing import Literal

# 1. Load base + LoRA adapter
BASE = "Qwen/Qwen2.5-0.5B-Instruct"
ADAPTER = "fuhao23/encoder_v0"

tok = AutoTokenizer.from_pretrained(BASE, trust_remote_code=True)
base = AutoModelForCausalLM.from_pretrained(
    BASE, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)
model_hf = PeftModel.from_pretrained(base, ADAPTER).eval()

# 2. Define output schema (must match the schema used in training)
class Temporal(BaseModel):
    t_ref: str = ""
    t_valid_from: str = ""
    t_valid_to: str = ""

class MemoryRecord(BaseModel):
    memory_type: Literal["fact", "preference", "event", "constraint",
                         "procedure", "failure_pattern", "tool_affordance"]
    semantic_text: str
    entities: list[str]
    temporal: Temporal
    tags: list[str]
    evidence_turns: list[int]
    source_role: Literal["user", "assistant", "both", ""]

class MemoryRecordList(BaseModel):
    records: list[MemoryRecord]

model = outlines.from_transformers(model_hf, tok)
generator = outlines.Generator(model, MemoryRecordList)

# 3. The system prompt this adapter was trained on (use COMPACT_ENCODING_SYSTEM
#    from LycheeMem: src/memory/semantic/prompts.py:13-85). Must use as-is.
SYSTEM_PROMPT = """You are a memory extractor for a personal AI assistant's
long-term memory system. ... (full prompt in LycheeMem repo)"""

# 4. Build user content + encode
user_content = """\
<PREVIOUS_TURNS>
(no previous turns)
</PREVIOUS_TURNS>

<CURRENT_TURNS>
user: I want to try out my new slow cooker from Bed Bath & Beyond.
assistant: Congratulations! Slow cookers are great for ...
user: Thanks for the cleaning tips.
</CURRENT_TURNS>"""

prompt = tok.apply_chat_template(
    [{"role": "system", "content": SYSTEM_PROMPT},
     {"role": "user", "content": user_content}],
    tokenize=False, add_generation_prompt=True,
)
output = generator(prompt, max_new_tokens=1024)
print(output)
# Output: strict JSON of {"records": [...]}
```

### Inference (without constrained decoding — not recommended)

The model **CAN** be used without `outlines`, but **schema compliance drops from 100% to ~64%** due to base Qwen2.5-0.5B's tendency to regress to conversation-continuation mode on assistant-advice-heavy inputs. Always use constrained decoding for production.

## Limitations

This is a v0 research release. **Read carefully before deployment**:

1. **LLM-as-judge bias in evaluation**. The `weighted_score` is computed using V4 Flash as judge — the same model family as the teacher. Comparisons against models stronger than V4 Flash (Qwen2.5-72B, DeepSeek-V3) may have ceiling effects; the precise SOTA ranking around rank 1-4 is not fully reliable.

2. **No human ground truth**. No human annotator has labeled records as "good / bad" — judge consistency with humans is unverified. Recommended next step: 50-sample human annotation + Cohen's kappa.

3. **No downstream retrieval evaluation**. The original training plan included an `evidence retrieval hit@10` benchmark on LongMemEval — this is not yet completed. The current metrics measure **encoder output quality in isolation**, not the end-to-end impact on memory retrieval accuracy.

4. **Narrow evaluation distribution**. The 519-sample held set is entirely English personal-dialog (LongMemEval + MSC). Chinese, technical, code, and long-context dialogs are not evaluated. OOD deployment may degrade.

5. **Entity coverage weakness**. `entity_coverage` 4-dim score is 2.93 vs SOTA 4.1-4.3 — the encoder under-extracts named entities. Planned fix in v2 with entity-rich training data.

6. **Constrained decoding is required for the headline 100% schema_ok**. Without `outlines`, schema compliance drops to ~64%.

7. **Not yet integrated into LycheeMem runtime**. No real-traffic data — quality on actual user dialogs vs the eval set is untested.

## Method Background

Pipeline and evaluation methodology documented in detail at the [LycheeMem repository](https://github.com/LycheeMem/lycheemem):

- `docs/encoder_v0.md` — full evaluation report with case studies
- `docs/encoder_eval_framework.md` — evaluation framework
- `examples/encoder_v0_try.py` — interactive try-it tool

Inspired by [MemReranker](https://arxiv.org/abs/2605.06132)'s small-model distillation methodology for memory systems.

## Citation

```bibtex
@misc{lycheemem_encoder_v0,
  title  = {Encoder v0: A Distilled Memory Encoder for Long-Term Conversation Memory},
  author = {LycheeMem},
  year   = {2026},
  url    = {https://huggingface.co/fuhao23/encoder_v0}
}
```

Base model:

```bibtex
@misc{qwen2.5,
  title  = {Qwen2.5: A Party of Foundation Models},
  author = {Qwen Team},
  year   = {2024}
}
```

## License

Apache 2.0 (matches base Qwen2.5-0.5B-Instruct license).