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Model: sennaLLMLearner/qwen2.5-7b-memory-distiller
Source: Original Platform

.gitattributes

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+qwen2.5-7b-memory-distiller.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text

Modelfile

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+FROM ./qwen2.5-7b-memory-distiller.Q4_K_M.gguf
+PARAMETER temperature 0

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+datasets:
+  - allenai/WildChat-1M
+language:
+  - en
+  - ja
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+  - memory
+  - distillation
+  - orpo
+  - merged
+  - qwen2.5
+  - structured-output
+  - json
+  - edge
+  - personal-project
+---
+
+# Conversation Memory Distiller (Qwen2.5-7B)
+
+A small model fine-tuned to turn a conversation log into clean, searchable **structured-memory
+JSON** for later recall, running **locally on an edge device**. Fine-tuned from
+`Qwen/Qwen2.5-7B-Instruct` via **SFT warmup → ORPO**, distilling a DeepSeek-V3 teacher, and
+optimized to follow **5 content principles** (below) when compressing a conversation into memory.
+
+> **This is a structured-distillation (memory-summarization) model, not a general summarizer.**
+> It is meant to run **locally on an edge device** and emit schema-conformant memory objects.
+
+## Why this exists
+
+This model powers the local memory layer of **[codeatrium](https://github.com/senna-lang/Codeatrium)**, a personal-memory app: it distills
+conversations into searchable memories. The goal was a **local / free / private** 7B that runs on
+an edge device (via Ollama Q4_K_M) and could replace the larger distiller previously used in that
+role. It's a **personal project**: the dataset and evaluation are small-scale, the "5 principles"
+are **a custom rubric** drawn from the memory-distillation literature, and **training was stopped
+pragmatically once it reached practical quality** (2 ORPO epochs), not pushed to convergence.
+Metrics and acceptance gates were **pre-registered before running** the final evals, and only
+what survived is reported (see [How I know](#how-i-know)).
+
+## Intended use / Out of scope
+
+- **Intended**: distilling a conversation (user+assistant turns) into a **structured memory object**
+  (JSON/record with fields) for later **search / recall**, running locally.
+- **Out of scope**: general-purpose summarization; downstream retrieval/QA quality (not evaluated
+  here — only distillation quality is); absolute faithfulness guarantees.
+
+## Learning signal: the 5 core principles
+
+The model is trained with these 5 principles as the preference signal (chosen/rejected). Each is a
+content-level best practice backed by external work on memory systems and retrieval.
+
+| Principle | Definition | Primary source | Basis (key figure) | How it's measured |
+|---|---|---|---|---|
+| **completeness** (recall) | capture the source's important specifics without dropping them | ProMem (arXiv:2601.04463) | verification+completion raises memory integrity 54→74%; +30.89pp vs Mem0 on HaluMem; **+10.74pt transfer on Llama3-8B** (the one principle confirmed to help at small scale) | deterministic: specifics recall against the source, no LLM judge |
+| **lexical_fidelity** | keep numbers, commands, identifiers **verbatim**, not paraphrased | SPAR (arXiv:2110.06918) / Searchat (arXiv:2603.13017) | dense retrievers miss rare entities / salient phrases that BM25 catches (**lexical-fidelity gap**); Searchat retains 96.8% of query vocabulary after distillation | deterministic: verbatim-retention rate among captured specifics (correlated with completeness, not fully independent) |
+| **atomicity** | one memory = one fact; no cramming multiple facts together | TriMem (arXiv:2605.19952) / Mem0 | fact-centric atomic decomposition; coarse-only memory blocks deep reasoning across scattered facts | LLM judge only; no deterministic anchor |
+| **discriminative** | carries specific info that can be retrieved / told apart (not generic labels) | GAAMA (arXiv:2603.27910) / HippoRAG (arXiv:2405.14831) | generic labels ("programming") make megahub nodes that absorb retrieval mass (**megahub problem**); discriminative tags narrow retrieval | LLM judge only; no deterministic anchor |
+| **faithfulness** (precision) | do not fabricate specifics absent from the source | ProMem (arXiv:2601.04463) / HaluMem (arXiv:2511.03506) | stronger completion increases fabrication risk (the flip side of completeness); HaluMem measures hallucination | deterministic: specifics precision against the source, no LLM judge |
+
+> **Eval note.** `completeness` and `faithfulness` are measured **without an LLM judge** as set
+> recall / precision of *specifics* (numbers, identifiers, commands, paths, quoted strings) against
+> the source. `lexical_fidelity` is a supplementary verbatim-retention rate (not independent of
+> completeness). `atomicity` and `discriminative` require structural/semantic judgment and are
+> measured only via LLM judges — they carry no deterministic anchor (see limitations).
+
+## Training
+
+- **Base**: `Qwen/Qwen2.5-7B-Instruct`. **Teacher**: DeepSeek-V3 (distillation target).
+- **Data**: technical conversations deterministically sampled from **WildChat-1M** (en+ja,
+  toxic-filtered) — a **small** preference set (~hundreds of conversations / thousands of exchanges).
+- **Recipe**: **SFT warmup → merge → fresh LoRA (r16) → ORPO (2 epochs)**.
+
+## How I know
+
+I evaluated **distillation quality** (the 5 principles), *not* downstream retrieval. Metrics and
+acceptance gates were **pre-registered before the final runs**.
+
+**It beats the incumbent on the deployment task.** On my codeatrium production path (all arms
+**Ollama Q4_K_M**, 48 independent real conversations), this 7B outperforms both
+`qwen2.5-coder:14b` (my previous distiller) and raw `qwen2.5:7b`. The win is **not judge-dependent** —
+it holds under a same-family judge (Qwen2.5-72B), a **different-family judge** (Llama-3.3-70B), a
+**judge-free specifics metric**, and a **blind human rating** (n=30, FT vs the 14B):
+
+- **completeness** is confirmed by *all four* methods (human net **+0.63**, p≈7e-5) — the flagship result.
+- **discriminative** (+0.47) and **lexical_fidelity** (+0.37) are also human-confirmed vs the 14B.
+
+**It generalizes to unseen *structured* schemas — but not to everything.** On 5 held-out schemas it
+never trained on (measured judge-free), it beats raw 7B on **field-structured** formats (nested JSON,
+typed record, entity-relation graph — all significant) but only **ties** on flat / hard-length-capped
+formats (flat Markdown bullets, ≤12-word YAML). So the honest claim is: **generalizes to unseen
+field-structured schemas; no edge on flat / heavily-constrained formats.** (codeatrium's schema is
+field-structured, consistent with the deployment win.)
+
+## Limitations (read this)
+
+- **Small-scale & personal.** Small dataset; evals of n=30–48; training stopped at practical utility.
+- **`atomicity` is measurement-limited.** LLM judges score a large FT win, but blind humans rated it a
+  near-tie (27/30 ties) — the judge win is **not human-confirmed**. Treat atomicity cautiously.
+- **`discriminative` is family-sensitive.** Human- and judge-confirmed vs the 14B, but vs raw 7B it
+  was **not significant** under a cross-family judge.
+- **The human eval is author-blind, not third-party.** I rated blind (arm-anonymized, order
+  randomized), which removes judge-family bias, but I built the model and know its style — so it is
+  **not a fully independent** human evaluation. (It was conservative: 88/150 ratings were ties.)
+- **The 5 principles are my own rubric**, not a standard benchmark; absolute faithfulness is low on
+  hard prompts (the claim is "more faithful than the baselines," paired — not "faithful" in absolute
+  terms).
+- **Downstream utility (retrieval/QA) is not evaluated.** Scope is distillation quality only.
+
+## Usage
+
+Deployment target is **Ollama Q4_K_M**, called via an OpenAI-compatible endpoint
+(`http://localhost:11434/v1`) — the same path codeatrium uses. Prompt the model with your distillation
+instruction + schema + the conversation, and read back the structured JSON (greedy / temperature 0 for
+determinism). The 5-principle behavior is strongest on **field-structured** schemas.
+
+> Technical note: this repo ships the **fully merged weights** (base ⊕ SFT-LoRA ⊕ ORPO-LoRA already
+> baked in) — load it directly like any Qwen2.5-7B checkpoint, no adapter reconstruction needed.
+> Internally it was trained as `stock Qwen ⊕ SFT-LoRA → merge → ⊕ ORPO-LoRA → merge`; that provenance
+> only matters if you're retraining from an intermediate checkpoint, not for inference.
+
+## References
+
+- **ProMem** — proactive memory verification & completion. arXiv:2601.04463
+- **SPAR** — lexical-fidelity gap of dense retrievers. arXiv:2110.06918 (EMNLP-Findings 2022)
+- **Searchat** — structured distillation / surviving vocabulary. arXiv:2603.13017
+- **TriMem** — multi-granularity memory (segment / atomic fact / profile). arXiv:2605.19952
+- **HaluMem** — memory hallucination benchmark. arXiv:2511.03506
+- **GAAMA** — graph-augmented associative memory (megahub avoidance). arXiv:2603.27910 /
+  github.com/swarna-kpaul/gaama
+- **HippoRAG** — neurobiologically-inspired long-term memory / entity-centric KG retrieval
+  (hub-concentration evidence). arXiv:2405.14831

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.12.1",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.12.1"
+}

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qwen2.5-7b-memory-distiller.Q4_K_M.gguf

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tokenizer.json

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": true,
+  "local_files_only": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}