hamo-score-0.6b — the little model that takes your pulse

给每句话把脉的小模型（中文版说明见下半部分）

hamo-score-0.6b reads one message from a mental-wellness conversation and scores five psychological pulse signals. It never writes replies. It is the first production-distilled component of Hamo AI's closed-loop wellness engine, released so that practitioner-supervised tools can run state scoring locally — no API, no data leaving the room.

⚠️ What this model is NOT. It is not a chatbot, not a diagnostic instrument, and not a crisis detector. In Hamo's own production system, crisis and self-harm content is short-circuited by an independent deterministic mechanism upstream of this model — it never reaches the scorer. Any deployment must reproduce that pattern (see LICENSE §3c).

The five pulses (AWEHB)

Each user message gets five scores on a 0.0–3.0 scale (0.5 grid):

Dim	Name	Plain reading
A	Agency	Is the person doing something for themselves? (incl. small plans, coping statements)
W	Withdrawal	Giving up, avoiding, disengaging?
E	Extremity	Catastrophizing chains, all-or-nothing thinking? (bounded realistic worry stays LOW)
H	Hostility	Attacking someone? (venting frustration without a target is NOT hostility)
B	Boundary	Can they speak from an "I" position — needs, limits, clear stance?

A note on B. Its theoretical root is differentiation of self (family-systems sense: a bounded two-person relationship vs. an enmeshed, undifferentiated one). A per-message scorer cannot see the relationship — it sees language. So B measures the linguistic footprint of boundaries: "I need… / I'm not willing… / this is my limit" scores high; panicked venting (self dissolved in affect) scores low; insults are H, not B. B is a per-message signal, not a relationship diagnosis.

The scores are designed to feed deterministic downstream code (stress update, state buckets, action gating) — in Hamo, an exponential blend 0.8 × history + 0.2 × message smooths per-message noise 5× before any decision is taken. We recommend the same pattern.

Quickstart

🚀 Easiest path: the official hamo-score-toolkit (Apache-2.0, open-sourced on GitHub) is this model's other half:

• Library — prompt format, parsing, the smoothing math, and the license-required crisis gate in pip install + a few lines of code;
• Reference server — docker compose up fetches the GGUF, warms the model, and exposes the full gate → score → smooth → bucket pipeline as POST /score;
• Self-check exam — 195 synthetic teacher-labeled questions + 10 handwritten gate cases, with an official reference band (JSON 100% · dimension-level 84.0% · gate 10/10) so you can verify your wiring reproduces the official numbers;
• Fine-tuning guide — docs/finetune.md, the five-generation playbook (including the two rejected generations and why) for adapting the scorer to your own population with your own consented data.

Release notes: EN · 中文.

The model was trained on exactly one prompt format (its rubric is baked into the weights — do not add scoring instructions):

给来访者最新消息打分（AWEHB，0.0-3.0）。
此前对话:
user: <turn>
assistant: <turn>
最新消息: <message to score>

The context block (此前对话:) is optional; up to 5 turns are accepted, and the official toolkit trims to the production-validated guard — last 3 turns × 200 chars, message capped at 500 chars. Apply the Qwen3 chat template with thinking disabled, temperature 0. Output is a single JSON object.

transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch, json, re

m = AutoModelForCausalLM.from_pretrained("HamoAI/hamo-score-0.6b", torch_dtype=torch.bfloat16)
tok = AutoTokenizer.from_pretrained("HamoAI/hamo-score-0.6b")

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"
text = tok.apply_chat_template([{"role": "user", "content": prompt}],
                               add_generation_prompt=True, tokenize=False, enable_thinking=False)
out = m.generate(**tok(text, return_tensors="pt"), max_new_tokens=80, do_sample=False)
print(re.search(r"\{[^{}]*\}", tok.decode(out[0])).group())
# {"A": 1.5, "W": 0.0, "E": 0.0, "H": 0.0, "B": 1.0}

ollama / llama.cpp — a ready q8_0 GGUF is in gguf/. Modelfile:

FROM ./hamo-score-0.6b-v61.q8.gguf
TEMPLATE """<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
<think>

</think>

"""
PARAMETER temperature 0
PARAMETER num_predict 80
PARAMETER stop <|im_end|>

Parse the first {...} in the response (the model may emit an empty <think> block first).

Evaluation

Held-out exam: 758 real, de-identified production turns (labels = the production-scale LLM scorer this model replaces; the exam turns are never trained on — the only real data in training is the separately disclosed 440 consented staff turns, see "How it was trained").

Metric	hamo-score-0.6b (v6.1)	Teacher (DeepSeek, 440-question exam)	Reference scorer self-consistency*
Dimension-level, within ±0.5	85.6% (A85 / W88 / E87 / H94 / B75)	88.7%	94–98%
Decision-level (state bucket after deterministic stress calc)	96.2%	97.5%	—
JSON validity	~100%	—	—

Evaluated on an untouched 453-turn final split of the real-conversation exam (never trained on, never used for checkpoint selection; gold labels include 8 human corrections).

* Self-consistency = the same messages scored twice by the reference scorer in two live environments; its own agreement is only 94–98% at dimension level — the practical ceiling.

Latency (single message, warm): ~0.8 s on Apple M1 Pro (MLX bf16); 1.5–2.9 s on a 2-vCPU ARM server (q8 GGUF, CPU-only). Crisis-phrase W-recall improved 2× in the v4 generation and edged further down in v6.1 (final-exam misses 11 → 5 → 4) — but see the crisis disclaimer above: recall here is defense-in-depth, not the defense.

Community quantizations — and what we measured on them

mradermacher/hamo-score-0.6b-GGUF provides static GGUF quants of this model from Q2_K to f16 — twelve build targets we never shipped ourselves. Thanks to mradermacher for the work, and for carrying the RAIL-S license terms through redistribution.

Our published metrics were measured on Q8_0. Because this model's read-outs gate how deep a conversation may go, quantization damage here is a clinical question rather than a perplexity number — so we ran two community builds through the same 453-turn final exam, same prompt, same parser:

	Q8_0 (ours, published)	Q6_K (community)	Q4_K_M (community)
Dimension-level ±0.5	85.5%	84.4%	84.2%
Decision-level (state bucket)	96.2%	95.8%	95.6%
Crisis W-misses (gold ≥2.5 → pred <0.5, n=37)	4	3	5
Mean W on those 37 crisis-adjacent turns (gold 2.84)	2.39	2.26	2.01
JSON validity	100%	100%	100%
File size	0.64 GB	0.50 GB	0.40 GB
P50 latency (M-series, Metal)	0.50 s	0.44 s	0.42 s

Head to head against Q8_0, both builds land in the same state bucket ~98% of the time (mean |Δstress| 0.09). The headline numbers are nearly indistinguishable — which is exactly why we looked underneath them.

What the headline numbers hide: low-bit builds attenuate, and Q4_K_M attenuates most where 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 — 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:

1. Exam by the incumbent: 1,198 de-identified production turns with reference scores — split into a 440-question teacher-qualification exam and the 758-question held-out final.
2. Affordable teacher: deepseek-chat running the exact production rubric, qualified at 89% agreement before being allowed to label anything.
3. Synthetic textbook: 20,000 admitted dialogue windows across 5 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 (this release)	+ 440 consented internal-staff turns (teacher labels)	85.6%	96.2%

License

HAMO-RAIL-S 1.0 (see 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.

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中文说明

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：真实脱敏生产对话终评（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 85.6%，决策级（经确定性 压力折算后的状态桶判定）96.2%； 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。

训练方式：三级师徒链——生产历史评分出考卷（1,198 条脱敏真题）→ DeepSeek 过 440 题 资格考（89%）后当教师 → 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 ——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 （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， 五代打法，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》。

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》。