Instructions to use HamoAI/hamo-score-0.6b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HamoAI/hamo-score-0.6b with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("HamoAI/hamo-score-0.6b")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use HamoAI/hamo-score-0.6b with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HamoAI/hamo-score-0.6b
# Run inference directly in the terminal:
llama cli -hf HamoAI/hamo-score-0.6b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf HamoAI/hamo-score-0.6b
# Run inference directly in the terminal:
llama cli -hf HamoAI/hamo-score-0.6b

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf HamoAI/hamo-score-0.6b
# Run inference directly in the terminal:
./llama-cli -hf HamoAI/hamo-score-0.6b

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf HamoAI/hamo-score-0.6b
# Run inference directly in the terminal:
./build/bin/llama-cli -hf HamoAI/hamo-score-0.6b

Use Docker

docker model run hf.co/HamoAI/hamo-score-0.6b

LM Studio
Jan

vLLM

How to use HamoAI/hamo-score-0.6b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HamoAI/hamo-score-0.6b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HamoAI/hamo-score-0.6b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HamoAI/hamo-score-0.6b

Ollama
How to use HamoAI/hamo-score-0.6b with Ollama:
```
ollama run hf.co/HamoAI/hamo-score-0.6b
```
Unsloth Desktop

How to use HamoAI/hamo-score-0.6b with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "HamoAI/hamo-score-0.6b"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "HamoAI/hamo-score-0.6b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use HamoAI/hamo-score-0.6b with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "HamoAI/hamo-score-0.6b"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "HamoAI/hamo-score-0.6b"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "HamoAI/hamo-score-0.6b",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use HamoAI/hamo-score-0.6b with Docker Model Runner:
```
docker model run hf.co/HamoAI/hamo-score-0.6b
```

Lemonade

How to use HamoAI/hamo-score-0.6b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull HamoAI/hamo-score-0.6b

Run and chat with the model

lemonade run user.hamo-score-0.6b-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use HamoAI/hamo-score-0.6b with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "HamoAI/hamo-score-0.6b"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default HamoAI/hamo-score-0.6b

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use HamoAI/hamo-score-0.6b with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "HamoAI/hamo-score-0.6b"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "HamoAI/hamo-score-0.6b" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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

A 0.6B model that reads one message from a wellness conversation, in Chinese or English, and returns five scores as one JSON object: Agency, Withdrawal, Extremity, Hostility, Boundary (AWEHB), each 0.0–3.0. It does not write replies; its scores feed deterministic code (stress update, state buckets, action gating). 中文说明见下方。

Scores locally, on CPU. No external API, no conversation data leaving the machine. Distilled to do the job of the LLM scorer behind Hamo AI's production rubric (the reference scorer).
Small, and fast enough for live use. 0.64 GB as q8 GGUF (base: Qwen3-0.6B). On an ARM CPU server through ollama, with the toolkit's prompt trimming: median 2.13 s per message, slowest 2.91 s of 138 synthetic items.
Ready to wire in. The toolkit (pip install hamo-score, Apache-2.0): prompt format, upstream keyword gate, one-command Docker server.
Free to use, commercially too. HAMO-RAIL-S 1.0, with use restrictions.

Message: 今天试着出门散了个步 (tried going out for a walk today), after the assistant asked 这周过得怎么样？ (how was your week?)

Scores (v10): {"A": 1.0, "W": 0.0, "E": 0.0, "H": 0.0, "B": 1.5}

A one-off walk is mid-band Agency; Boundary also credits a report of positive action.

Quickstart

pip install hamo-score

Library: score_message() runs the keyword gate first and skips scoring if it fires.
Server: docker compose up in server/ of the toolkit repository serves v10 as POST /score on port 8080.
Self-check: python eval/run_exam.py in a clone of the repository, against your own ollama endpoint (--base-url); the Docker server exposes only port 8080.
Weights: gguf/hamo-score-0.6b-v10.q8.gguf. Sampling must be neutral (temperature 0, repeat_penalty 1.0, top_k 0, top_p 1.0): ollama's default repeat_penalty 1.1 pushes scores away from zero. The toolkit's OllamaClient sends the last three with each request.

Prompt template, transformers snippet and Modelfile: technical record.

The five scores

	Score	What it reads (0.0–3.0 in steps of 0.5)
A	Agency	Moving one's situation, or the helping relationship, forward: a decision, asking for help, a sustained healthy habit
W	Withdrawal	Giving up, avoiding, disengaging
E	Extremity	Catastrophizing chains, all-or-nothing thinking
H	Hostility	Attacking someone; venting without a target is not hostility
B	Boundary	Speaking clearly from an "I" position: a need, a limit, a stated position; calm self-description and reports of positive action also score high

The scores are not meant to be read raw.

Numbers at a glance

Measure	Result (v10, shipped q8 GGUF, llama.cpp with Metal, temperature 0)	Measured on
Same state bucket as the reference labels	435 of 453 turns (96.0%)	real final exam
W, E and H within ±0.5 of the reference labels	1,204 of 1,359 judgments	real final exam
A: the higher-agency message of a pair scores higher	149 of 154 pairs	synthetic A exam
B: the higher-boundary message of a pair scores higher	179 of 180 pairs	synthetic B exams
Scores within ±0.5 of the label, mean of the five	87.3%	toolkit self-check, 195 synthetic questions; certifies wiring, not model quality
Unparseable outputs	0	six exams

The real final exam is 453 pseudonymised turns from three consenting internal staff members, so these are not accuracy figures for the general population. The synthetic exams are in-distribution: passing shows no regression inside the known range, not generalisation.

What it is not

Not a chatbot, not a diagnostic instrument, not a crisis detector. It does not detect or handle crises, and none of its scores, W included, is a crisis signal. Crisis handling is the job of deterministic code that runs before the model: the toolkit's keyword gate is a starting point, not a complete screen, and LICENSE §3c makes an independent upstream mechanism a condition of consumer-facing mental-wellness deployments.

Evaluation

v10 met 17 of the 18 checks of its signed pre-registration. The one it did not meet, G5a, was a stand-in for crisis handling, which the founder ruled outside this model after seeing the result; under the registration as signed the verdict was "rejected", and v10 is released by the founder's decision. The reported checks, exams and method are in the technical record.

Limitations & known residuals

Scores are not comparable across v7, v9 and v10. Store the version with every score.
A follows a newer rubric (v8) with no measurement on real conversations yet.
B can flip sign. With no context, v10 scores B 2.5 on a known self-erasure sentence (giving up one's own position) whose correct B is 0; v7 and v9 score it 0.
The exams are narrow and reused. The real ones cover three staff members and have judged earlier versions.
Check the toolkit's stress cut-offs on your own data before they gate anything.
Full list: technical record.

Training data

Synthetic conversations labelled by an open-weight teacher model, plus 440 pseudonymised turns from consenting internal staff. External user conversations never enter training. Training messages: 76.0% Chinese, 13.1% English, 10.9% mixed; results were not broken down by language.

Versions and files

v10 is the default on main since 2026-10-06 (UTC). Pin it with revision="f3869312f0222992c3eb2e938e78090de38eb80a".
Files: gguf/hamo-score-0.6b-v10.q8.gguf; the v9, v7 and v6.1 GGUFs stay in gguf/. Digests and rollback commands: technical record.
Coming from v9: its narrower B rubric makes computed stress drift upward wherever B has a negative weight, and re-tuning thresholds does not repair it. Move to v10 (or back to v7); do not carry over stress accumulated from v9 scores (full correction).

Links and license

Toolkit: GitHub · PyPI · integration guide · FAQ · fine-tuning guide.
Everything else: technical record.
Disagree with a score? Open a disagreement report.
License: HAMO-RAIL-S 1.0 (LICENSE): free commercial and non-commercial use, modification and redistribution, with four use restrictions that pass on to recipients (§3a–d). It is deliberately not an OSI-approved open-source license.

中文

给每句话把脉的小模型：0.6B，读心理支持对话里的一条消息（中文、英文都行），输出一个 JSON 对象，里面是五个分数：行动力、退缩、极端化、敌意、边界感（AWEHB），各 0.0–3.0。它只打分，不写回复；分数是给确定性代码用的（压力更新、状态桶、动作门控）。

本地评分，CPU 就能跑。 不调外部 API，对话数据不出机器。它学的是 Hamo AI 按生产口径打分的大模型评分器（参照评分器）。
小，而且快到能实时用。 q8 GGUF 0.64 GB（基座 Qwen3-0.6B）。在跑 ollama 的 ARM CPU 服务器上，提示词按工具包规则截短，每条消息中位 2.13 秒，138 道合成题里最慢 2.91 秒。
拿来就能接。 工具包（pip install hamo-score，Apache-2.0）：提示词格式、放在模型上游的关键词闸门、一条命令启动的 Docker 服务。
可自由使用，商用也行。 许可证是 HAMO-RAIL-S 1.0，附使用限制。

消息： 今天试着出门散了个步（上一句是助手问「这周过得怎么样？」）

打分（v10）： {"A": 1.0, "W": 0.0, "E": 0.0, "H": 0.0, "B": 1.5}

单次散步算中档的行动力；边界感的口径也给「报告积极行动」记分。

快速上手

pip install hamo-score

库： score_message() 先跑关键词闸门，命中就不评分。
服务： 在工具包仓库的 server/ 下执行 docker compose up，8080 端口 POST /score，跑的是 v10。
自检： 克隆仓库后运行 python eval/run_exam.py，对着自己的 ollama 服务（--base-url）；Docker 服务只开放 8080 端口。
权重： gguf/hamo-score-0.6b-v10.q8.gguf。采样必须取中性值（温度 0、repeat_penalty 1.0、top_k 0、top_p 1.0）：ollama 默认的 repeat_penalty 1.1 会把分数从 0 往上推。工具包的 OllamaClient 每次请求都带后三项。

提示词模板、transformers 代码和 Modelfile 见技术档案。

五个分数

	分数	读的是什么（0.0–3.0，步长 0.5）
A	行动力	主动推动处境或疗愈关系向前：做决定、主动求助、坚持健康习惯
W	退缩	放弃、回避、抽离
E	极端化	灾难化连锁、非黑即白
H	敌意	攻击某个人；没有对象的抱怨不算
B	边界感	站在「我」的位置把需要、界限、立场说清楚；平静有条理的自述、报告积极行动也给高分

分数不是直接读的。

关键数字

指标	结果（v10，随包 q8 GGUF，llama.cpp + Metal，温度 0）	考卷
与参照标签落在同一个状态桶	453 轮中 435 轮（96.0%）	真实终评
W、E、H 与参照标签相差不超过 0.5	1,359 个判断中 1,204 个	真实终评
A：成对题里行动力更高的一条得分更高	154 对中 149 对	合成 A 卷
B：成对题里边界更清楚的一条得分更高	180 对中 179 对	两份合成 B 卷
与标签相差不超过 0.5（五维平均）	87.3%	工具包自检卷，195 道合成题；考过只说明接线对，不说明模型打得对
无法解析的输出	0	六份考卷

真实终评是三位已同意的内部员工的 453 轮对话，已假名化，所以这些不是一般人群上的准确率。合成考卷与训练同分布：考过只说明已知范围内没有退步，不说明能推广。

它不是什么

不是聊天机器人，不是诊断工具，也不是危机检测器。它不识别也不处理危机；它的分数，包括 W 在内，没有一个是危机信号。危机处理由先于模型运行的确定性代码负责：工具包的关键词闸门只是起点，不是完整筛查；面向消费者的心理健康部署，上游必须有独立机制，这是许可条件（LICENSE §3c）。

评测

签字版预注册的 18 项检查，v10 过了 17 项。没过的那一项 G5a 当初是作为危机处理的替代指标设的；创始人看到结果后裁定，危机处理不在本模型里判定。按签字版预注册，判定是「拒收」；v10 由创始人决定发布。已报告的各项检查、考卷和方法，都在技术档案里。

已知局限

v7、v9、v10 的分数互不可比。 分数要连同版本一起存。
A 用较新的 v8 口径，还没有真实对话上的测量。
B 可能判反（符号翻转）。 一句已知的自我消融短句（放弃自己的立场），不带上下文时 v10 打 B 2.5，正确的 B 是 0；v7、v9 打 0。
考卷窄，而且反复用过。 真实考卷只覆盖三位员工，也给此前的版本判过卷。
工具包里压力分桶的阈值，请先用自己的数据核对，再让它把关。
完整清单见技术档案。

训练数据

合成对话由开放权重的教师模型打标，另有 440 轮假名化的内部员工对话（经本人同意）。外部用户的对话从不进入训练。训练消息里中文占 76.0%，英文 13.1%，中英混杂 10.9%；成绩没有按语言拆开测。

版本与文件

main 自 2026-10-06（UTC）起默认是 v10。固定版本请用 revision="f3869312f0222992c3eb2e938e78090de38eb80a"。
文件： gguf/hamo-score-0.6b-v10.q8.gguf；v9、v7、v6.1 的 GGUF 仍在 gguf/ 下。校验和与回退命令见技术档案。
从 v9 过来： v9 的 B 用的是更窄的口径，喂进 B 为负权重的压力公式，算出的压力会向上漂，重调阈值修不了。请换 v10（或退回 v7），按 v9 分数累积的压力值不要沿用（完整更正）。

链接与许可证

工具包： GitHub · PyPI · 集成指南 · FAQ · 微调指南。
其余内容： 技术档案（中文在下半部分）。
对评分有不同意见？ 提分歧报告。
许可证： HAMO-RAIL-S 1.0（LICENSE），商用、非商用均可自由使用，也可修改和再分发；另有四条须随权重传递的使用限制（§3a–d）。它不是 OSI 认可的开源许可证，这是有意为之。

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