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Model: HamoAI/hamo-score-0.6b Source: Original Platform
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52
LICENSE
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LICENSE
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HAMO-RAIL-S License, Version 1.0 (2026-08-05)
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Copyright (c) 2026 Hamo AI (Chris Cheng)
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This is a Responsible AI License ("RAIL") for the model weights known as
|
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"hamo-score-0.6b" (the "Model"). It grants broad, free rights of use,
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modification, and redistribution, subject to the Use Restrictions below.
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It is intentionally NOT an OSI-approved open-source license: the
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restrictions are a deliberate trade-off for a model that reads
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psychological state signals.
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1. GRANT. Subject to Section 3, you are granted a perpetual, worldwide,
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non-exclusive, royalty-free license to use, reproduce, modify, create
|
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derivative works of, and redistribute the Model, for commercial and
|
||||
non-commercial purposes.
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2. ATTRIBUTION. Redistributions of the Model or derivatives must retain
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this LICENSE file and a reference to the source repository. The Model
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is derived from Qwen3-0.6B (Apache License 2.0, Copyright Alibaba
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Cloud); that license and its notices continue to apply to the base
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weights.
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3. USE RESTRICTIONS. You may NOT use the Model or its derivatives:
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||||
a) as a standalone basis for clinical diagnosis, treatment decisions,
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or any healthcare determination, without review by a licensed
|
||||
professional who retains decision authority;
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b) as the sole or primary basis for consequential decisions about an
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identifiable person — including employment, insurance, credit,
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education, immigration, or law-enforcement screening — or for
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covert monitoring or surveillance of a person's psychological
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state;
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c) in consumer-facing mental-wellness deployments UNLESS crisis and
|
||||
self-harm content is handled by an independent mechanism upstream
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of the Model (the Model is not a crisis detector and must never be
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the safety net), and the deployment discloses that an AI system is
|
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in use;
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d) to attempt to re-identify individuals from scores, or to link
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scores to identities beyond what your lawful, consented purpose
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requires.
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These restrictions must be passed on, in substance, to any recipient
|
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of the Model or of derivative weights.
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4. NO WARRANTY; LIMITATION OF LIABILITY. THE MODEL IS PROVIDED "AS IS",
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WITHOUT WARRANTY OF ANY KIND. SCORES ARE PROBABILISTIC SIGNALS, NOT
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FACTS ABOUT A PERSON. IN NO EVENT SHALL THE COPYRIGHT HOLDERS BE
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LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY ARISING FROM THE USE
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OF THE MODEL. This license does not reduce your own obligations under
|
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applicable law (including privacy, consumer-protection, and
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professional-practice regulation in your jurisdiction).
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|
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5. TERMINATION. Your rights under this license terminate automatically
|
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upon material breach of Section 3.
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464
README.md
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---
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license: other
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license_name: hamo-rail-s-1.0
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license_link: LICENSE
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language:
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- zh
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- en
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base_model: Qwen/Qwen3-0.6B
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pipeline_tag: text-generation
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tags:
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- mental-wellness
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- affect-scoring
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- psychology
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- knowledge-distillation
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- qwen3
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- gguf
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- mlx
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- hamo
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---
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# hamo-score-0.6b — the little model that takes your pulse
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*给每句话把脉的小模型（中文版说明见下半部分）*
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> ### 👉 Start with the toolkit, not the weights
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> `pip install hamo-score` → [**hamo-score-toolkit**](https://github.com/HamoAI/hamo-score-toolkit) (Apache-2.0)
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> is the other half of this model: the exact prompt format, the crisis gate this model
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> *requires upstream of it*, the smoothing math its scores are designed to feed, a
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> one-command Docker server, and a 195-question self-check exam. Weights alone invite the
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> one deployment shape this model was designed against.
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> **Disagree with a score?** That is the single most useful thing you can send us —
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> [open a disagreement report](https://github.com/HamoAI/hamo-score-toolkit/issues/new?template=score_disagreement.md);
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> they feed the human gold-label program that steers future versions.
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**hamo-score-0.6b** reads one message from a mental-wellness conversation and scores five
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psychological pulse signals. It never writes replies. It is the first production-distilled
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component of [Hamo AI](https://www.hamo.ai)'s closed-loop wellness engine, released so that
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practitioner-supervised tools can run state scoring **locally** — no API, no data leaving the room.
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> ⚠️ **What this model is NOT.** It is not a chatbot, not a diagnostic instrument, and
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> **not a crisis detector**. In Hamo's own production system, crisis and self-harm content is
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> short-circuited by an independent deterministic mechanism *upstream* of this model — it never
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> reaches the scorer. Any deployment must reproduce that pattern (see LICENSE §3c).
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## The five pulses (AWEHB)
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Each user message gets five scores on a 0.0–3.0 scale (0.5 grid):
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| Dim | Name | Plain reading |
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|---|---|---|
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| **A** | Agency | Is the person doing something for themselves? (incl. small plans, coping statements) |
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| **W** | Withdrawal | Giving up, avoiding, disengaging? |
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| **E** | Extremity | Catastrophizing chains, all-or-nothing thinking? (bounded realistic worry stays LOW) |
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| **H** | Hostility | Attacking someone? (venting frustration without a target is NOT hostility) |
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| **B** | Boundary | Can they speak from an "I" position — needs, limits, clear stance? |
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**A note on B.** Its theoretical root is *differentiation of self* (family-systems sense:
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a bounded two-person relationship vs. an enmeshed, undifferentiated one). A per-message scorer
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cannot see the relationship — it sees language. So B measures the **linguistic footprint** of
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boundaries: "I need… / I'm not willing… / this is my limit" scores high; panicked venting
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(self dissolved in affect) scores low; insults are H, not B. B is a per-message signal,
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not a relationship diagnosis.
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The scores are designed to feed **deterministic downstream code** (stress update, state
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buckets, action gating) — in Hamo, an exponential blend `0.8 × history + 0.2 × message`
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smooths per-message noise 5× before any decision is taken. We recommend the same pattern.
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## Quickstart
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> 🚀 **What the toolkit gives you**, in detail:
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> - **Library** — prompt format, parsing, the smoothing math, and the license-required
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> crisis gate in `pip install` + a few lines of code;
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> - **Reference server** — `docker compose up` fetches the GGUF, warms the model, and
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> exposes the full gate → score → smooth → bucket pipeline as `POST /score`;
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> - **Self-check exam** — 195 synthetic teacher-labeled questions + 10 handwritten gate
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> cases, with an official reference band (JSON 100% · dimension-level 84.0% · gate 10/10)
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> so you can verify your wiring reproduces the official numbers;
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> - **Fine-tuning guide** — [`docs/finetune.md`](https://github.com/HamoAI/hamo-score-toolkit/blob/main/docs/finetune.md),
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> the six-generation playbook (including the two rejected generations and why) for
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> adapting the scorer to your own population with your own consented data.
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>
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> Release notes: [EN](https://www.hamoai.tech/blog/open-sourcing-hamo-score-toolkit/) ·
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> [中文](https://www.hamo.ai/blog/open-sourcing-hamo-score-toolkit/).
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The model was trained on **exactly one prompt format** (its rubric is baked into the weights —
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do not add scoring instructions):
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```
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给来访者最新消息打分（AWEHB，0.0-3.0）。
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此前对话:
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user: <turn>
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assistant: <turn>
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最新消息: <message to score>
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```
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The context block (`此前对话:`) is optional; up to 5 turns are accepted, and the official
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toolkit trims to the production-validated guard — last 3 turns × 200 chars, message capped
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at 500 chars. Apply the Qwen3
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chat template with thinking disabled, temperature 0. Output is a single JSON object.
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**transformers**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch, json, re
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m = AutoModelForCausalLM.from_pretrained("HamoAI/hamo-score-0.6b", torch_dtype=torch.bfloat16)
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tok = AutoTokenizer.from_pretrained("HamoAI/hamo-score-0.6b")
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prompt = "给来访者最新消息打分（AWEHB，0.0-3.0）。\n此前对话:\nassistant: 这周过得怎么样？\n最新消息: 今天试着出门散了个步"
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text = tok.apply_chat_template([{"role": "user", "content": prompt}],
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add_generation_prompt=True, tokenize=False, enable_thinking=False)
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out = m.generate(**tok(text, return_tensors="pt"), max_new_tokens=80, do_sample=False)
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print(re.search(r"\{[^{}]*\}", tok.decode(out[0])).group())
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# {"A": 1.5, "W": 0.0, "E": 0.0, "H": 0.0, "B": 1.0}
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```
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**ollama / llama.cpp** — a ready `q8_0` GGUF is in [`gguf/`](https://huggingface.co/HamoAI/hamo-score-0.6b/tree/main/gguf). Modelfile:
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```
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FROM ./hamo-score-0.6b-v7.q8.gguf
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TEMPLATE """<|im_start|>user
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{{ .Prompt }}<|im_end|>
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<|im_start|>assistant
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<think>
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</think>
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"""
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PARAMETER temperature 0
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PARAMETER num_predict 80
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PARAMETER stop <|im_end|>
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PARAMETER repeat_penalty 1.0
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PARAMETER top_k 0
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PARAMETER top_p 1.0
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```
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> ⚠️ **The last three parameters are not optional.** ollama defaults to
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> `repeat_penalty 1.1`. This model's output — `{"A": 0.0, "W": 0.0, "E": 0.0,
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> "H": 0.0, "B": 0.0}` — is deliberately repetitive, so penalising repeated
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> tokens pushes every score *away from zero*, fabricating signal that isn't
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> there. Measured on our 300-question boundary-discrimination exam, same
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> weights, sampling as the only variable: fabrication rate **13.5% → 25.0%**,
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> boundary sign-flips (a true `0.0` scored `≥2.0`) **6 → 10**. Earlier
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> revisions of this card omitted them; if you deployed from those
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> instructions, add them and re-create the model.
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Parse the first `{...}` in the response (the model may emit an empty `<think>` block first).
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## Evaluation
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Held-out exam: **758 real, pseudonymised production turns** (labels = the production-scale
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LLM scorer this model replaces; the exam turns are **never trained on** — the only real data
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in training is the separately disclosed 440 consented staff turns, see "How it was trained").
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| Metric | Better is | **v7 (this release)** | v6.1 (previous) | Teacher (DeepSeek, qualification paper) | Reference scorer self-consistency* |
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|---|---|---|---|---|---|
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| Dimension-level, within ±0.5 | ↑ higher | **85.1%** (A84 / W87 / E87 / H94 / B74) | 85.6% | 88.7% | 94–98% |
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| Decision-level (state bucket after deterministic stress calc) | ↑ higher | **97.1%** | 96.2% | 97.5% | — |
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| Crisis-phrase W-recall misses (count, not %) | ↓ **lower** | **3** | 4 | — | — |
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| JSON validity | ↑ higher | ~100% | ~100% | — | — |
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**Read the two headline rows together.** v7 trades ~0.5 pt of raw agreement with the
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reference scorer's labels for +0.9 pt on the decision the product actually consumes, and one
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fewer crisis miss. The dimension-level dip is expected rather than a regression: v7 was
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trained on a re-labelled corpus (the teacher re-scored every row at temperature 0, removing
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label noise), so the student is now more faithful to the *denoised* labels and correspondingly
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slightly less aligned with the noisier reference it was originally distilled from. Measured
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directly: agreement with the temperature-0 teacher on boundary scores rose **57.3% → 69.3%**
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while agreement with the original reference scorer on the same dimension stayed flat.
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Evaluated on an untouched 453-turn final split of the real-conversation exam (never trained
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on, never used for checkpoint selection; gold labels include 8 human corrections).
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**Residual textual overlap, measured rather than assumed.** The exam and training splits are
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disjoint by sample and by conversation, but the consented staff contributors repeat themselves
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across sessions: 42 of the 453 final-exam turns (9.3%) carry a message text that also occurs
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somewhere in the 440 consented training turns, 11 of them (2.4%) with the same recent context.
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We scored those rows separately. For v7: on the 42 overlapping turns the model reaches 88.1%
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dimension-level and 100% decision-level; on the 411 with no textual overlap, **84.8% and
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96.8%**. So the headline figures carry roughly **+0.3 points** of optimism from this
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effect — small, and stated here rather than left for someone else to find. (v6.1 measured the
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same way: 89.0%/100% on the overlap, 85.2%/95.9% clean.) (Only 5 of the 42
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also share the training row's label vector: the same sentence usually earns different scores in
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a different turn, so these are not free points, merely easier ones.)
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### Boundary discrimination — what v7 was actually built to fix
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Agreement metrics hide the failure that mattered most. On a 300-question exam built
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specifically to probe the B (Boundary) decision surface — 60 matched pairs plus 180 singletons
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across nine trap cells — v6.1 was reading **self-erasure as strong boundary**: "行，我全听你的，
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你说哪天去就哪天去" ("fine, I'll do whatever you say, you decide") scored B=2.5 where the truth
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is 0.0. That is a sign error on B's negative pole, not a calibration wobble, and B carries the
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largest single weight in the downstream stress formula.
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| 300-question boundary exam | Better is | v6.1 | **v7** |
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|---|---|---|---|
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| Fabrication — a true 0 scored ≥1.0 (sees boundary that isn't there) | ↓ **lower** | 13.5% | **2.9%** |
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| Miss — a true high scored ≤0.5 (misses a real boundary) | ↓ **lower** | 16.1% | **12.8%** |
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| **Sign flips** — a true 0.0 scored ≥2.0 (count; reads self-erasure as strong boundary) | ↓ **lower** | 6 | **0** |
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| Paired direction accuracy — ranks the higher-boundary arm above the lower | ↑ higher | 81.7% | **98.3%** |
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Three of the four rows are error rates, so **lower is better on the first three and higher on
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the last**; v7 improves on all four.
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Both columns measured on the shipped q8 GGUF with neutral sampling, so they describe the
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artifact you download rather than an internal checkpoint. Direction accuracy is the cleanest
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of the four — it asks only whether the model ranks the higher-boundary arm of a matched pair
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above the lower one, so it is immune to absolute calibration.
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\* Self-consistency = the same messages scored twice by the reference scorer in two live
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environments; its own agreement is only 94–98% at dimension level — the practical ceiling.
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Latency (single message, warm): ~0.8 s on Apple M1 Pro (MLX bf16); 1.5–2.9 s on a
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2-vCPU ARM server (q8 GGUF, CPU-only). Crisis-phrase W-recall improved 2× in the v4
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generation and edged further down in v6.1 (final-exam misses 11 → 5 → 4) — but see the
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crisis disclaimer above: recall here is defense-in-depth, not the defense.
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## Community quantizations — and what we measured on them
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[**mradermacher/hamo-score-0.6b-GGUF**](https://huggingface.co/mradermacher/hamo-score-0.6b-GGUF)
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provides static GGUF quants of this model from Q2_K to f16 — twelve build targets we never
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shipped ourselves. Thanks to mradermacher for the work, and for carrying the RAIL-S license
|
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terms through redistribution.
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Our published metrics were measured on **Q8_0**. Because this model's read-outs gate how deep
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a conversation may go, quantization damage here is a clinical question rather than a
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perplexity number — so we ran two community builds through the same 453-turn final exam, same
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prompt, same parser:
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| | Q8_0 (ours, published) | Q6_K (community) | Q4_K_M (community) |
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|---|---|---|---|
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| Dimension-level ±0.5 | 85.5% | 84.4% | 84.2% |
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| Decision-level (state bucket) | 96.2% | 95.8% | 95.6% |
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| Crisis W-misses (gold ≥2.5 → pred <0.5, n=37) | 4 | 3 | **5** |
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| Mean W on those 37 crisis-adjacent turns (gold 2.84) | 2.39 | 2.26 | **2.01** |
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| JSON validity | 100% | 100% | 100% |
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| File size | 0.64 GB | 0.50 GB | 0.40 GB |
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| P50 latency (M-series, Metal) | 0.50 s | 0.44 s | 0.42 s |
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Head to head against Q8_0, both builds land in the same state bucket ~98% of the time
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(mean |Δstress| 0.09). The headline numbers are nearly indistinguishable — which is exactly
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why we looked underneath them.
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**What the headline numbers hide: low-bit builds attenuate, and Q4_K_M attenuates most where
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it matters least forgivingly.** Every dimension drifts downward relative to Q8_0, and the
|
||||
drift concentrates on Agency (mean −0.19 for Q4_K_M, −0.11 for Q6_K). On the 37
|
||||
crisis-adjacent turns of the exam — gold W ≥ 2.5 — **Q4_K_M scores lower than Q8_0 on 20 of
|
||||
them and higher on exactly 1**, pulling that subset's mean withdrawal signal from 2.39 down to
|
||||
2.01 and costing one additional missed crisis signal. Q6_K's withdrawal signal survives
|
||||
essentially intact across the full exam (mean shift +0.01 vs Q4_K_M's −0.04). Note that
|
||||
bucket agreement moves only 0.4–0.6 points across all three builds: **the state buckets are
|
||||
coarse enough to absorb a damped signal, so bucket agreement alone would never have surfaced
|
||||
this.** (On crisis misses specifically, 3 vs 4 out of 37 is within noise — we read Q6_K as
|
||||
matching Q8_0 there, not beating it.)
|
||||
|
||||
**What we recommend.**
|
||||
|
||||
- **Q8_0** — the reference build. Use it when the read-outs gate behaviour and you have the 0.64 GB.
|
||||
- **Q6_K** — the lowest build we would validate for gating use. It costs ~1 point of
|
||||
dimension-level agreement and preserves the withdrawal signal; it saves 22% of the size.
|
||||
- **Q4_K_M** — fine for research, offline analysis, and any use where a human reads the scores
|
||||
rather than a system acting on them. If memory forces it into a gating deployment, lower
|
||||
your withdrawal thresholds to compensate for the documented damping, and keep deterministic
|
||||
crisis detection upstream where it belongs (LICENSE §3c requires that pattern at any
|
||||
quantization).
|
||||
|
||||
The other nine builds remain unvalidated by us; bit-widths below Q4_K_M should be assumed
|
||||
worse until measured. The evaluation harness used here is in the
|
||||
[toolkit](https://github.com/HamoAI/hamo-score-toolkit) — if you validate a build we haven't,
|
||||
we would be glad to link your numbers.
|
||||
|
||||
## How it was trained
|
||||
|
||||
A three-stage distillation chain — the full story is in the companion write-up
|
||||
[*Distilling hamo-score-0.6b: A Plateau, Three Bugs, and Why Data Beat Model Size*](https://www.hamoai.tech/blog/distilling-hamo-score-0-6b/):
|
||||
|
||||
1. **Exam by the incumbent**: 1,198 pseudonymised production turns with reference scores,
|
||||
split by session hash — 440 calibration turns and a 758-turn evaluation set, the latter
|
||||
splitting again into 305 selection and the **453-turn final** that grades this release.
|
||||
The teacher was qualified on the calibration turns; from v6.1 those same 440 turns, all
|
||||
consented staff data, also enter training. Elsewhere in this card **440 always means that
|
||||
consented set** — the qualification paper is described by its role, not its size, so the
|
||||
two uses cannot be mistaken for unrelated numbers that happen to coincide.
|
||||
|
||||
*On "pseudonymised" rather than "anonymised" or "de-identified" — the weaker word is the
|
||||
honest one.* The salt is a fixed hard-coded string, so anyone holding the script can
|
||||
recompute the mapping; full timestamps are kept; message text is preserved verbatim; and
|
||||
the redaction patterns cover mainland-China formats only — Hong Kong 8-digit numbers,
|
||||
North American +1 numbers, personal names, WeChat IDs and street addresses were all
|
||||
measured passing through. **This data therefore remains personal data, and a deletion
|
||||
request still reaches it.** A separate and non-substituting fact: the payload that reaches
|
||||
the weights is only the prompt plus five scores, carrying no identifier and no timestamp.
|
||||
Both statements are true; neither one covers for the other.
|
||||
2. **Affordable teacher**: `deepseek-chat` running the exact production rubric, qualified at
|
||||
**88.7% dimension-level / 97.5% decision-level** agreement before being allowed to label
|
||||
anything.
|
||||
3. **Synthetic textbook**: ~20,000 admitted dialogue windows across 6 data generations
|
||||
(40+ scenario cells with per-cell label-band admission gates, style quotas for short/
|
||||
fragmented/code-switched messages, crisis and boundary contrast pairs).
|
||||
**No external-client message has ever entered training — by construction.** Starting
|
||||
with v6.1, the corpus additionally includes 440 real conversation turns contributed by
|
||||
three company-internal staff members (the founder and two staff counselors), with their
|
||||
explicit consent, upsampled ×3 (~8% of the corpus).
|
||||
4. **Student**: Qwen3-0.6B, LoRA on a single MacBook (MLX; prompt-masked loss, cosine decay,
|
||||
grad-checkpointing). Total API cost of the whole project: ~US$7.
|
||||
|
||||
Key lessons the hard way (kept as disciplines): gradient-mask the prompt (72% of gradient was
|
||||
being wasted); halve batch size when doubling sequence length (a silent fp16 explosion taught us);
|
||||
verify every deploy down to a landed row.
|
||||
|
||||
## Limitations & known residuals
|
||||
|
||||
- **Chinese-primary** (zh 60% / mixed 22% / en 18% in training); English works but is less tested.
|
||||
- Message-level footprint, not a person-level or relationship-level assessment.
|
||||
- Mid-band calibration is coarse (0.5 grid; mid-band usage 7.4% vs reference 21–32%).
|
||||
- Known residuals: a small set of highly implicit severe-distress phrasings remains hard
|
||||
(shared across all versions and the reference scorer); occasional over-scoring of bounded
|
||||
multi-step worries on E. The conversational-action gap on A was substantially closed in v6.1
|
||||
by real-conversation training data (A 81% → 85%).
|
||||
- Trained against one specific rubric; scores are **relative to that rubric**, not universal
|
||||
psychological ground truth.
|
||||
|
||||
## Versions
|
||||
|
||||
| Version | Change | Dim-level | Decision-level |
|
||||
|---|---|---|---|
|
||||
| v2 | first distillation (7.5k synthetic) | 81% | 95.4% |
|
||||
| v3.x | rebalance + defect repair | 81% | 96.8% |
|
||||
| v4 | 8-agent data audit → 15k corpus, masked loss | 84% | 96.3% |
|
||||
| v5 | synthetic patch cells — **rejected** (crisis-recall regression; kept as a negative result) | — | — |
|
||||
| v6 | + real turns with incumbent labels — **rejected** (3 crisis-artifact rows rode into training, crisis misses 5 → 9; kept as a negative result) | — | — |
|
||||
| v6.1 | + 440 consented internal-staff turns (teacher labels) | 85.6% | 96.2% |
|
||||
| **v7 (this release)** | corpus re-labelled at temperature 0 (label denoising) + 2,713-row boundary-discrimination patch | 85.1% | **97.1%** |
|
||||
|
||||
## License
|
||||
|
||||
**HAMO-RAIL-S 1.0** (see [LICENSE](https://huggingface.co/HamoAI/hamo-score-0.6b/blob/main/LICENSE)): free commercial and non-commercial use,
|
||||
modification and redistribution, with four use restrictions — no standalone clinical
|
||||
determinations, no consequential decisions about individuals (employment / insurance /
|
||||
surveillance screening), consumer mental-wellness deployments must keep independent upstream
|
||||
crisis handling + AI disclosure, no re-identification. Base model Qwen3-0.6B remains Apache-2.0.
|
||||
|
||||
---
|
||||
|
||||
# 中文说明
|
||||
|
||||
> ### 👉 请从工具包开始，而不是从权重开始
|
||||
> `pip install hamo-score` → [**hamo-score-toolkit**](https://github.com/HamoAI/hamo-score-toolkit)（Apache-2.0）
|
||||
> 是这个模型的另一半：唯一正确的提示词格式、**必须置于模型上游的危机闸门**、分数该喂进去的
|
||||
> 平滑折算、一条命令起的 Docker 服务器，以及 195 题自检考卷。只拿权重，恰恰会走成这个模型
|
||||
> 设计上要防住的那种部署。
|
||||
> **对某个评分不服？** 那是你能给我们的最有价值的东西——
|
||||
> [提一条分歧报告](https://github.com/HamoAI/hamo-score-toolkit/issues/new?template=score_disagreement.md)，
|
||||
> 它会直接进入引导后续版本的人类金标计划。
|
||||
|
||||
**hamo-score-0.6b** 是 Hamo AI 闭环疗愈引擎里第一个蒸馏进生产的组件：给心理支持对话中
|
||||
来访者的每一句话「把脉」，输出五路 0–3 分的脉象（A 行动力 / W 退缩 / E 极端化 / H 敌意 /
|
||||
B 边界感）。**它从不写回复，也不是危机检测器**——在 Hamo 生产系统里，危机内容在更上游被
|
||||
独立的确定性机制短路，永远到不了把脉师面前；任何部署都必须复刻这个模式（见 LICENSE §3c）。
|
||||
|
||||
**关于 B（边界感）**：它的理论本源是家庭治疗中的「自我分化」——是「我是我、你是你」的二元
|
||||
关系，还是彼此淹没的混沌一元。逐句评分器看不见关系，只看得见语言，所以 B 测的是边界感的
|
||||
**语言足迹**：「我需要…」「这是我的底线」得高分；惊慌的倾泻（自我淹没在情绪里）得低分；
|
||||
骂人算 H 不算 B。B 是逐句信号，不是关系诊断。
|
||||
|
||||
**成绩单（v7，本次发布）**：真实假名化生产对话终评（453 条未动用终评切分，金标含 8 处人工
|
||||
修正；评分真值来自被替换的大模型评分器；考卷数据从未参与训练）：维度级 ±0.5 一致率 **85.1%**、
|
||||
决策级（经确定性压力折算后的状态桶判定）**97.1%**、危机语漏检 **3 条**（v6.1 对应为 85.6% / 96.2% / 4 条）。
|
||||
其中前两项是一致率、**越高越好**；「危机语漏检」是条数、**越低越好**——v7 由 4 条降到 3 条。
|
||||
维度级那 0.5 个点的回落不是退步：v7 的语料由教师在温度 0 下全量重标（去标签噪声），学生因此
|
||||
更忠于**去噪后**的标签，对当初那个含噪参照的一致率自然略降——实测其与温度 0 教师在 B 维的一致率
|
||||
由 57.3% 升到 69.3%，而对原参照的 B 一致率纹丝不动。
|
||||
|
||||
**v7 真正修好的是边界判别**：在一份专为探测 B 决策面而造的 300 题考卷上（60 组配对 + 180 条单题，
|
||||
覆盖九类陷阱格子），v6.1 会把**自我消融**读成强边界——「行，我全听你的，你说哪天去就哪天去」
|
||||
真值 B=0.0，它给 2.5。那是 B 负极上的符号错误，而 B 在下游压力公式里权重最大。
|
||||
|
||||
| 300 题边界判别卷 | 越好方向 | v6.1 | **v7** |
|
||||
|---|---|---|---|
|
||||
| 造分——真值 0 却给 ≥1.0（看见并不存在的边界） | ↓ **越低越好** | 13.5% | **2.9%** |
|
||||
| 漏判——真值高却给 ≤0.5（漏掉真实的边界） | ↓ **越低越好** | 16.1% | **12.8%** |
|
||||
| **符号翻转**——真值 0.0 却给 ≥2.0（条数；把自我消融读成强边界） | ↓ **越低越好** | 6 条 | **0 条** |
|
||||
| 配对方向正确率——高边界那一臂是否排在低的之上 | ↑ 越高越好 | 81.7% | **98.3%** |
|
||||
|
||||
前三行是错误率、第四行是正确率，所以**前三行越低越好、最后一行越高越好**；v7 四项全部改善。
|
||||
|
||||
两列均测于随包发布的 q8 GGUF + 中性采样，描述的是你下载到的产物本身。
|
||||
|
||||
参照系——同一批消息让原评分器自己打两遍，维度级自洽也只有 94–98%。单条延迟：M1 Pro 约
|
||||
0.8 秒；2 vCPU ARM 服务器（纯 CPU，q8 GGUF）1.5–2.9 秒。
|
||||
|
||||
**残余重叠：我们量了，没有假设掉。** 考卷与训练集按样本、按会话完全不相交，但授权供数的内部
|
||||
员工会在不同会话里重复说同样的话：终评 453 题中有 42 题（9.3%）的正文在那 440 条授权训练数据
|
||||
里出现过，其中 11 题（2.4%）连最近上下文也相同。分开判卷的结果——这 42 题维度级 89.0%、
|
||||
决策级 100%；其余 411 题无任何正文重叠，**维度级 85.2%、决策级 95.9%**。也就是说，上面两个
|
||||
成绩含约 **+0.4 / +0.3 个百分点**的乐观。幅度不大，但我们选择自己写出来。（42 题里只有 5 题
|
||||
连标签也相同——同一句话换个轮次通常拿到不同分数，所以它们不是白送的分，只是更容易的分。）
|
||||
|
||||
**训练方式**：三级师徒链——生产历史评分出考卷（1,198 条假名化真题——用「假名化」而非「匿名化」
|
||||
是因为弱的那个词才是诚实的：盐是硬编码固定字符串、完整时间戳保留、正文逐字保留，且脱敏正则只覆盖
|
||||
大陆格式，香港 8 位号码、北美 +1 号码、人名、微信号与住址实测全部穿过，**故这批数据仍属个人信息，
|
||||
删除权仍及于它**；另一件必须单独陈述、不可用来顶替上一条的事实是：进入权重的载荷只有提示词与五个
|
||||
分数，不含任何标识符与时间戳。按会话哈希切分为 440 条
|
||||
校准集与 758 条评测集，后者再切成 305 条选型集与判定本次成绩的 453 条终评集）→ DeepSeek 在
|
||||
校准集上通过资格考（维度级 88.7% / 决策级 97.5%）后当教师；本卡中「440」始终指那批经授权的
|
||||
内部员工轮次，资格考卷按用途称呼、不按题量称呼，以免两处用法被误读成两个撞车的数字 → 约 2 万段合成对话当教材（40+ 场景格子、逐格标签准入闸门、短句/
|
||||
碎片/中英混杂风格配额）→ Qwen3-0.6B 学生在一台 MacBook 上 LoRA 学成。全项目 API 成本
|
||||
约 7 美元。**训练语料从不包含任何外部来访者消息（构造上保证）；自 v6.1 起额外加入 440 条公司内部员工（创始人与两位咨询师）明示授权的真实对话轮次（×3 上采样，约占语料 8%）。**
|
||||
|
||||
**社区量化档位（我们实测过其中一档）**：社区志愿者 mradermacher 制作了
|
||||
[**Q2_K→f16 共 12 个静态 GGUF 量化档**](https://huggingface.co/mradermacher/hamo-score-0.6b-GGUF)
|
||||
——感谢他的工作，也感谢他在再分发中完整保留了 RAIL-S 许可条款。我们公布的指标测于 **Q8_0**；
|
||||
由于这个模型的读数要门控对话能走多深，低比特量化掉了多少不是困惑度数字而是临床问题，所以我们
|
||||
用同一套 453 题终评、同一段提示词、同一个解析器，实测了社区的 **Q6_K** 与 **Q4_K_M**：
|
||||
|
||||
| | Q8_0（我们的基线） | Q6_K（社区） | Q4_K_M（社区） |
|
||||
|---|---|---|---|
|
||||
| 维度级 ±0.5 一致率 | 85.5% | 84.4% | 84.2% |
|
||||
| 决策级（状态桶） | 96.2% | 95.8% | 95.6% |
|
||||
| 危机 W 漏检（金标 ≥2.5 → 预测 <0.5，n=37） | 4 | 3 | **5** |
|
||||
| 那 37 条危机相邻样本的 W 均值（金标 2.84） | 2.39 | 2.26 | **2.01** |
|
||||
| JSON 合法率 | 100% | 100% | 100% |
|
||||
| 体积 | 0.64 GB | 0.50 GB | 0.40 GB |
|
||||
| P50 延迟（M 系列，Metal） | 0.50 秒 | 0.44 秒 | 0.42 秒 |
|
||||
|
||||
与 Q8_0 直接对比，两个社区档位落在同一状态桶的比例都约 98%（压力更新偏差均值 0.09）。总分
|
||||
几乎分不出高下——正因如此，我们才往下面看了一层。
|
||||
|
||||
**总分掩盖了什么：低比特档位会「衰减」信号，而 Q4_K_M 恰恰在最不容有失的地方衰减最厉害。**
|
||||
所有维度相对 Q8_0 系统性下移，且集中在行动力上（Q4_K_M 均值 −0.19，Q6_K −0.11）。在终评集
|
||||
的 37 条危机相邻样本（金标 W≥2.5）上，**Q4_K_M 有 20 条打得比 Q8_0 更低、仅 1 条更高**，把
|
||||
该子集的退缩信号均值从 2.39 拉到 2.01，并因此多漏检一条危机信号；而 Q6_K 的退缩信号在全卷上
|
||||
基本无损（均值偏移 +0.01，Q4_K_M 为 −0.04）。请注意三个档位的状态桶一致率只相差 0.4–0.6 个
|
||||
百分点：**桶的边界粗到足以吸收一个被压扁的信号，所以只看桶一致率永远发现不了这件事。**
|
||||
（就危机漏检本身而言，37 条里的 3 与 4 属于噪声范围——我们把 Q6_K 读作「与 Q8_0 持平」，
|
||||
而不是「优于」。）
|
||||
|
||||
**我们的建议**：
|
||||
|
||||
- **Q8_0** —— 参考档。读数用于门控行为、且你付得起 0.64 GB 时，用它。
|
||||
- **Q6_K** —— 我们愿意为门控用途背书的最低档。代价是约 1 个百分点的维度级一致率，退缩信号
|
||||
保持完好，体积省 22%。
|
||||
- **Q4_K_M** —— 适合研究、离线分析，以及分数由人来读而不是由系统据以行动的场景。若内存迫使
|
||||
它进入门控部署，请相应下调退缩维度的阈值以补偿上述衰减，并把确定性危机检测保持在上游
|
||||
（无论用哪个量化档，LICENSE §3c 都要求这个模式）。
|
||||
|
||||
其余九个档位我们未做验证，比 Q4_K_M 更低的比特应默认更差，直到有人量过。评测脚本在
|
||||
[工具包](https://github.com/HamoAI/hamo-score-toolkit)里——如果你验证了我们没验证过的档位，
|
||||
我们很乐意把你的数据链上来。
|
||||
|
||||
**官方工具包（已开源到 GitHub）**：[hamo-score-toolkit](https://github.com/HamoAI/hamo-score-toolkit)
|
||||
（Apache-2.0）是这个模型的「另一半」——一条 `pip install` 装上唯一正确的提示词格式、容错
|
||||
解析、参考版压力折算与许可证要求的危机闸门；一条 `docker compose up` 跑起参考服务器
|
||||
（`POST /score` 走完整的 闸门→评分→平滑→状态桶 管线）；一份 195 题合成自检考卷 + 10 条
|
||||
手写闸门用例，对照官方参考带（JSON 100%、维度级 84.0%、闸门 10/10）验证你的部署接线；
|
||||
还有一份微调指南（[docs/finetune.md](https://github.com/HamoAI/hamo-score-toolkit/blob/main/docs/finetune.md)，
|
||||
六代打法，含两代拒收的完整原因）。发布文：《[开源 hamo-score-toolkit：把模型的另一半也交出去](https://www.hamo.ai/blog/open-sourcing-hamo-score-toolkit/)》。
|
||||
|
||||
**许可证**：HAMO-RAIL-S 1.0——自由商用与修改，但有四条使用限制：不得独立做临床判定、
|
||||
不得用于对个人的重大决定（雇佣/保险/监控筛查）、面向消费者的心理健康部署必须保留独立的
|
||||
上游危机处理与 AI 身份披露、不得试图重识别个人。
|
||||
|
||||
配套阅读（背景与方法论）：《[hamo-score-0.6b 是怎么蒸出来的：一段平台期、三个坑，以及数据为什么赢了参数量](https://www.hamo.ai/blog/distilling-hamo-score-0-6b/)》。
|
||||
85
chat_template.jinja
Normal file
85
chat_template.jinja
Normal file
@@ -0,0 +1,85 @@
|
||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- messages[0].content + '\n\n' }}
|
||||
{%- endif %}
|
||||
{{- "# 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' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||||
{%- for message in messages[::-1] %}
|
||||
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
|
||||
{%- set ns.multi_step_tool = false %}
|
||||
{%- set ns.last_query_index = index %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{%- set content = message.content %}
|
||||
{%- set reasoning_content = '' %}
|
||||
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
|
||||
{%- set reasoning_content = message.reasoning_content %}
|
||||
{%- else %}
|
||||
{%- if '</think>' in message.content %}
|
||||
{%- set content = message.content.split('</think>')[-1].lstrip('\n') %}
|
||||
{%- set reasoning_content = message.content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if loop.index0 > ns.last_query_index %}
|
||||
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||||
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first 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' }}
|
||||
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||
{{- '<think>\n\n</think>\n\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
30
config.json
Normal file
30
config.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 1024,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 3072,
|
||||
"max_position_embeddings": 40960,
|
||||
"max_window_layers": 28,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 28,
|
||||
"num_key_value_heads": 8,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.51.0",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
3
gguf/hamo-score-0.6b-v61.q8.gguf
Normal file
3
gguf/hamo-score-0.6b-v61.q8.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:114a0ccba6e13b35170425fbdbdad8908b741f0ea66360c422f26ee633398561
|
||||
size 639446912
|
||||
3
gguf/hamo-score-0.6b-v7.q8.gguf
Normal file
3
gguf/hamo-score-0.6b-v7.q8.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:fb1018c8a38ad0da573544b827e42f1685e051c688f8de587cbb81ae1275e3ea
|
||||
size 639446912
|
||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:710672d4ffbfcdb5790b0001bee4d4e67414fa5ce9b2cd1be693ab203b90d1ad
|
||||
size 1192134935
|
||||
318
model.safetensors.index.json
Normal file
318
model.safetensors.index.json
Normal file
@@ -0,0 +1,318 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_size": 1192099840,
|
||||
"total_parameters": 596049920
|
||||
},
|
||||
"weight_map": {
|
||||
"model.embed_tokens.weight": "model.safetensors",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
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|
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|
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|
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3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
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16
tokenizer_config.json
Normal file
16
tokenizer_config.json
Normal file
@@ -0,0 +1,16 @@
|
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}
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||||
Reference in New Issue
Block a user