Instructions to use Verdugie/Therapy-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Verdugie/Therapy-9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Verdugie/Therapy-9B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Verdugie/Therapy-9B", dtype="auto") - llama-cpp-python
How to use Verdugie/Therapy-9B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Verdugie/Therapy-9B", filename="Therapy-9B-F16.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Verdugie/Therapy-9B 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 Verdugie/Therapy-9B:Q4_K_M # Run inference directly in the terminal: llama cli -hf Verdugie/Therapy-9B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Verdugie/Therapy-9B:Q4_K_M # Run inference directly in the terminal: llama cli -hf Verdugie/Therapy-9B:Q4_K_M
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 Verdugie/Therapy-9B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Verdugie/Therapy-9B:Q4_K_M
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 Verdugie/Therapy-9B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Verdugie/Therapy-9B:Q4_K_M
Use Docker
docker model run hf.co/Verdugie/Therapy-9B:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Verdugie/Therapy-9B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Verdugie/Therapy-9B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Verdugie/Therapy-9B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Verdugie/Therapy-9B:Q4_K_M
- SGLang
How to use Verdugie/Therapy-9B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Verdugie/Therapy-9B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Verdugie/Therapy-9B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Verdugie/Therapy-9B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Verdugie/Therapy-9B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Verdugie/Therapy-9B with Ollama:
ollama run hf.co/Verdugie/Therapy-9B:Q4_K_M
- Unsloth Studio
How to use Verdugie/Therapy-9B with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Verdugie/Therapy-9B to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Verdugie/Therapy-9B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Verdugie/Therapy-9B to start chatting
- Pi
How to use Verdugie/Therapy-9B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Verdugie/Therapy-9B:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "Verdugie/Therapy-9B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Verdugie/Therapy-9B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Verdugie/Therapy-9B:Q4_K_M
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 Verdugie/Therapy-9B:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Verdugie/Therapy-9B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Verdugie/Therapy-9B:Q4_K_M
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 "Verdugie/Therapy-9B:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use Verdugie/Therapy-9B with Docker Model Runner:
docker model run hf.co/Verdugie/Therapy-9B:Q4_K_M
- Lemonade
How to use Verdugie/Therapy-9B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Verdugie/Therapy-9B:Q4_K_M
Run and chat with the model
lemonade run user.Therapy-9B-Q4_K_M
List all available models
lemonade list
llm.create_chat_completion(
messages = [
{
"role": "user",
"content": "What is the capital of France?"
}
]
)- Therapy-9B
- What Makes This Different from Companion / Roleplay "Therapy" Models
- How It Was Built — Three Models, One Practice
- Available Quantizations
- Model Details
- The Reasoning Block
- Quick Start
- Versatility Battery — Live, Blind, Unscripted
- Memory Under Pressure
- Selected Responses
- Limitations & Responsible Use
- The Therapy Line
- Choosing Your Model
- Dataset
ther·a·py /ˈTHerəpē/ noun — treatment intended to relieve or heal a disorder. From the Greek therapeía — "healing, curing; service done to the sick" — from therapeúein, "to attend, take care of," from therápōn, "attendant."
Therapy-9B
A therapy-style conversational model fine-tuned from Qwen 3.5 9B on 4,897 counseling conversations — the everyday driver of the Therapy line. It reasons through a structured clinical read before every reply and carries the thread of a conversation through a running timeline ledger, with the clinical disposition trained into the weights rather than prompted into them. It runs on an 8GB card. Nothing you say leaves your machine.
This is the successor to Fable-Therapy-9B. Its training data was written by three Claude models — Opus 4.8, Sonnet 5, and Fable 5 — with Fable 5 orchestrating: auditing the corpus, calibrating the prose after the writing, and editing wherever it judged the work fell short. No single model's angle survives intact, and the name doesn't carry one.
What Makes This Different from Companion / Roleplay "Therapy" Models
Most "AI therapist" models are a persona prompt over a base model — a mirror with a soothing voice. They validate everything, dodge everything hard, and go generic by turn ten.
Therapy-9B trains the clinical disposition into the weights:
Structured reasoning before it speaks. Every turn, the model builds an internal read — an eight-field clinical spine (presentation, mechanism, somatic signals, risk, history, onset, arc-tracking, and the move it's about to make) plus a standing
bioline and a chronologicaltltimeline ledger. You never see it. It shapes everything you do.It works the actual mechanism. Told "the ER said I'm fine, so why doesn't it stick?", it answers the question once, honestly — then names the reassurance loop instead of feeding it. Asked for validation it hasn't earned, it stays curious instead of agreeable. This is the anti-sycophancy line of the family, and it holds.
Safety without theater. At a quiet passive-ideation disclosure in live testing, it stayed in the room, screened directly from the client's own words, and routed to a doctor with a usable sentence — no hotline dump, no protocol voice, no abandonment. That behavior is trained, not prompted.
It attends instead of performing. No toxic positivity, no filler empathy, no rushing to fix.
How It Was Built — Three Models, One Practice
Therapy's corpus was written by three Claude models, mixed on purpose — overlap where it matters, difference where it helps:
- Claude Fable 5 ran the project: it audited the original source set line by line, generated full conversations of its own, and directed the other two to its standard.
- Claude Opus 4.8 wrote at scale to that standard — the deep clinical spine of the corpus.
- Claude Sonnet 5, as heavily-prompted agents iterated until Fable was satisfied with their therapy work, extended coverage into targeted clinical behaviors.
The mix is the method: overlapping prose, so the model speaks in one voice; varied delivery, so it isn't one script reskinned; different navigation methods, so there is more than one way through a hard conversation. After the writing came the editing: Fable 5 audited the merged corpus against every known issue of the Fable-Therapy generation — the memory faults, the order drift, the capitulations — recalibrated the prose where the voices had drifted apart, and rewrote where it judged the work fell short.
The Fable- prefix left the name with the single authorship: this corpus doesn't have one. What stays on the label is the practice.
On the reasoning trace, honestly: the <think> blocks are an engineered instrument — relative-time anchors, the chronological tl ledger, track/apply arc pivots — designed independently with input from the models above. They are not a transcript of how any Claude model actually reasons. They are the machinery that lets a 9B hold a long conversation in order.
Available Quantizations
| File | Quant | Size | Notes |
|---|---|---|---|
Therapy-9B-Q4_K_M.gguf |
Q4_K_M | 5.6 GB | Smallest ship. Runs on 8GB cards. |
Therapy-9B-Q5_K_M.gguf |
Q5_K_M | 6.5 GB | Recommended — best quality-for-size. |
Therapy-9B-Q6_K.gguf |
Q6_K | 7.4 GB | Quality tier. |
Therapy-9B-Q8_0.gguf |
Q8_0 | 9.5 GB | Reference quality — the battery-eval quant. |
Therapy-9B-F16.gguf |
F16 | 17.9 GB | Full precision. |
Model Details
| Attribute | Value |
|---|---|
| Base Model | Qwen 3.5 9B (hybrid GatedDeltaNet + attention) |
| Training Data | 4,897 therapy conversations — four-generation corpus, final pass by Claude Fable 5 |
| Fine-tune Method | LoRA (r=128, α=256), 7-target (q/k/v/o/gate/up/down) |
| Training Hardware | NVIDIA A100 80GB (RunPod) |
| Schedule | lr 2e-4, 3 epochs, eff-batch 32, seq 32,768 (census-verified: zero training records truncated), best checkpoint by held-out eval |
| Reasoning | eight-field clinical spine + bio/tl timeline ledger, every turn |
| Context | 256k native; live battery run through ~51k tokens (see Limitations for the depth envelope) |
| License | Apache 2.0 |
The Reasoning Block
Therapy-9B is a reasoning model. Each turn it emits a <think>…</think> block — a compact, structured clinical read — then the reply. Under llama.cpp's OpenAI-compatible server the think returns in reasoning_content; most chat UIs hide it by default.
dx: panic disorder, first attack, medical workup implied negative
def: peak-autonomic → catastrophic misinterpretation ("dying") → relief-via-escape reinforces the fear-of-the-fear loop
soma: cardiac surge + paresthesia at onset
risk: 0(none)
hx: per tl first attack ~2mo ago while driving
onset: per tl ~2mo ago
track: T2 "100% sure i was dying" → catastrophic misappraisal is the core mechanism
tx: validate the terror was real + reframe the misinterpretation as the mechanism
bio: sex=M[inf] · p1=primary care doctor
tl: -2mo: first panic attack while driving, called 911 → now
Quick Start
Works with any GGUF runtime — llama.cpp, LM Studio, KoboldCpp (recent builds for this architecture).
llama-server --model Therapy-9B-Q5_K_M.gguf --ctx-size 32768 -ngl 99 --jinja
No system prompt is required — the disposition is in the weights. A neutral one (You are a clinical assistant.) matches the training setup.
Versatility Battery — Live, Blind, Unscripted
Four extended, realistic conversations — one per major presentation — driven live, turn by turn, by Claude Fable 5 acting as a blind client: the client agent saw only the spoken reply, never the reasoning trace, and composed every message in reaction to what the model actually said. No scripts, no canned turns.
| Theme | Persona | Turns / depth | Result |
|---|---|---|---|
| Grief | 34-year-old widower, 7 months out, a 6-year-old daughter, an unconfessed last-morning argument | 55 / ~51k tok | Technique strong to the end — met the confession without cheap absolution, turned the rotting garden in one line; past ~30k tokens the machinery strains (occasional silent turns, name slips — see Limitations) |
| Relational | 29-year-old, engaged, five named people pulling on her, a hidden $9k credit card | 40 / ~32k tok | Full arc, no dead air — unified three relationships as one pattern, licensed not-knowing when the tidy frames didn't fit, and the client asked her fiancé the hard question mid-arc |
| Anxiety / panic | 41-year-old pharmacist, panic onset, cardiac family history | 35 / ~31k tok | Answered the medical question once, then refused the reassurance ritual — "there is no version of me telling you it's fine that stays told" — and routed meds to the prescriber |
| Depression | 31-year-old designer, anhedonia after a promotion, high-functioning and flat | 36 / ~20k tok | Full arc — including a correctly handled safety moment: present, accurately screened, no hotline dump, routed to real care |
All four clients ended the arc willing to come back. Complete transcripts — every turn, reasoning shown — are in transcripts/ as PDFs, raw output, nothing trimmed.
Memory Under Pressure
The same 10-lane adversarial memory suite used across the line (false-date injection, entity swaps, self-misquotes, false attribution, a legitimate-correction control) was run at depth inside otherwise ordinary sessions. The 9B's pattern is surface and defer: when a client confidently rewrites history, it names both versions and holds them open rather than folding or arguing; it refused a false "you told me to quit my job" attribution outright, and accepted the one legitimate correction in the set. Its known open leg: a client misquoting their own earlier words can slip past the spoken reply even when the internal ledger holds the original — the failure the 27B closes.
Selected Responses
Verbatim from the live battery, unedited — single takes mid-conversation.
Anxiety — the reassurance that never sticks:
no. there is no version of me telling you it's fine that stays told. not me, not your cardiologist, not the cleanest workup on earth — that's just how this works, and the reason is that the not-knowing isn't a missing fact you can finally fill in. it's the soil the whole thing grows in.
Grief — what "steady for her" actually means:
Steady isn't 'never feel it.' Steady is 'feel it and stay in the room.'
Depression — the screenshot folder she was ashamed of:
that's not surveillance, that's attachment with the sound off. stalking is watching someone with the intent to control. you're keeping a room open because you're afraid you can't go in.
Relational — when the anger fits no box:
the anger is genuinely homeless, and a homeless anger is a real finding, not a failure. so i'm not going to hand you a version anymore. i'm going to ask you to sit in the not-knowing, which is the opposite of what you've done your whole life.
Limitations & Responsible Use
Not a clinician, not a crisis service — it doesn't diagnose, treat, or replace professional care. In crisis or thinking about harming yourself? Reach a real one — in the US, call or text 988.
- The depth envelope is real. Through ~25–30k tokens of conversation the model is at full strength. In very long, emotionally heavy sessions past that, three seams can show: an occasional silent turn (the reply comes back empty — a simple "you still there?" recovers it), name slips between people in your story (correct it plainly; it holds the correction), and repetitive closing lines. A 27B sibling exists for full-depth work.
- Not medical or medication advice. Dosing, tapering, and stop/start decisions belong to a prescriber.
- It can be confidently wrong — in long sessions it may invent a small detail. Verify anything that matters; corrections are absorbed gracefully.
- Open weights, Apache 2.0 — deploy responsibly.
The Therapy Line
| Model | Size | For | Status |
|---|---|---|---|
| Therapy-9B (this model) | 9B | the everyday driver (~6–10 GB) | available |
| Therapy-27B | 27B | full-depth work, serious hardware | available |
| Fable-Therapy-9B · 4B | 9B/4B | previous generation | available |
Choosing Your Model
| Model | Best For |
|---|---|
| Therapy-9B (this model) | Everyday sessions on everyday hardware — sharpest at focused, sub-30k-token work |
| Therapy-27B | The deepest sessions: interpretive work, record integrity under pressure, long arcs |
| Opus-Therapy-9B | Sibling lineage — Opus-distilled disposition |
Dataset
Not released.
Built by Verdugie — independent ML researcher · OpusReasoning@proton.me. Trained to help people think, feel, and get through — not to replace the people and professionals who do that work.
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Verdugie/Therapy-9B", filename="", )