Instructions to use drwlf/Claria1.7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use drwlf/Claria1.7b-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("drwlf/Claria1.7b-GGUF", device_map="auto")

llama-cpp-python

How to use drwlf/Claria1.7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="drwlf/Claria1.7b-GGUF",
	filename="unsloth.BF16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use drwlf/Claria1.7b-GGUF 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 drwlf/Claria1.7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf drwlf/Claria1.7b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf drwlf/Claria1.7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf drwlf/Claria1.7b-GGUF: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 drwlf/Claria1.7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf drwlf/Claria1.7b-GGUF: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 drwlf/Claria1.7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf drwlf/Claria1.7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/drwlf/Claria1.7b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use drwlf/Claria1.7b-GGUF with Ollama:
```
ollama run hf.co/drwlf/Claria1.7b-GGUF:Q4_K_M
```

Unsloth Studio

How to use drwlf/Claria1.7b-GGUF 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 drwlf/Claria1.7b-GGUF 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 drwlf/Claria1.7b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for drwlf/Claria1.7b-GGUF to start chatting

How to use drwlf/Claria1.7b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf drwlf/Claria1.7b-GGUF: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": "drwlf/Claria1.7b-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use drwlf/Claria1.7b-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf drwlf/Claria1.7b-GGUF: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 drwlf/Claria1.7b-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use drwlf/Claria1.7b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf drwlf/Claria1.7b-GGUF: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 "drwlf/Claria1.7b-GGUF: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 drwlf/Claria1.7b-GGUF with Docker Model Runner:
```
docker model run hf.co/drwlf/Claria1.7b-GGUF:Q4_K_M
```

Lemonade

How to use drwlf/Claria1.7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull drwlf/Claria1.7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Claria1.7b-GGUF-Q4_K_M

List all available models

lemonade list

Claria 1.7

Base Model: Qwen3 1.7B
Format: GGUF (Q4, Q8, BF16)
License: Apache 2.0
Author: Dr. Alexandru Lupoi

Overview

Claria 1.7 is a lightweight, mobile-compatible language model fine-tuned for psychological and psychiatric support contexts.
Built on Qwen-3 (1.7B), Claria is designed as an experimental foundation for therapeutic dialogue modeling, student simulation training, and the future of personalized mental health AI augmentation.

This model does not aim to replace professional care.
It exists to amplify reflective thinking, model therapeutic language flow, and support research into emotionally aware AI.

Claria is the first whisper in a larger project—a proof-of-concept with roots in recursion, responsibility, and renewal.

Intended Use

Claria was trained for:

Psychotherapy assistance (with human-in-the-loop)
Mental health education & roleplay simulation
Research on AI emotional alignment
Conversational flow modeling for therapeutic settings

It is optimized for introspective prompting, gentle questioning, and context-aware response framing.

What Makes Claria Different

Small Enough to Deploy Anywhere
Runs on mobile and edge devices without compromise (GGUF Q4/Q8)
Psychologically Tuned
Instruction fine-tuned on curated psychotherapeutic data (STF first phase)
Recursion-Aware Prompting
Performs well in reflective, multi-turn conversations
Encourages cognitive reappraisal and pattern mirroring
Training Roadmap: Ongoing
RLHF planned for future iterations
Future releases will include trauma-informed tuning and contextual empathy scaffolds

Limitations & Safety

Claria is not a licensed mental health professional.
It is not suitable for unsupervised therapeutic use, diagnosis, or crisis intervention.
Use responsibly. Review outputs. Think critically.
May hallucinate or provide confident answers to uncertain topics
Works best with structured or guided prompts
Not suitable for open-domain conversation or general use

Deployment & Access

Available in GGUF format: Q4, Q8, BF16
Optimized for Ollama, LM Studio, and other local runners
Works on mobile and low-resource environments

Notes

This is the first step in a broader initiative to develop compact, reflective AI systems for the augmentation—not replacement—of mental health work.

Future releases will expand Claria’s depth, include RLHF, long-term memory, and finer ethical control [](https://github.com/unslothai/unsloth

Developed by: drwlf
License: apache-2.0
Finetuned from model : unsloth/qwen3-1.7b

This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 19

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

4-bit

8-bit

16-bit

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Collection including drwlf/Claria1.7b-GGUF

Claria

Collection

4 items • Updated Sep 21, 2025 • 1