Instructions to use drwlf/Claria-14b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use drwlf/Claria-14b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="drwlf/Claria-14b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("drwlf/Claria-14b")
model = AutoModelForCausalLM.from_pretrained("drwlf/Claria-14b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use drwlf/Claria-14b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "drwlf/Claria-14b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drwlf/Claria-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/drwlf/Claria-14b

SGLang

How to use drwlf/Claria-14b 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 "drwlf/Claria-14b" \
    --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": "drwlf/Claria-14b",
		"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 "drwlf/Claria-14b" \
        --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": "drwlf/Claria-14b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use drwlf/Claria-14b 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/Claria-14b 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/Claria-14b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for drwlf/Claria-14b to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="drwlf/Claria-14b",
    max_seq_length=2048,
)

Docker Model Runner
How to use drwlf/Claria-14b with Docker Model Runner:
```
docker model run hf.co/drwlf/Claria-14b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Claria 14b

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

Overview

Claria 14b is a lightweight, mobile-compatible language model fine-tuned for psychological and psychiatric support contexts.
Built on Qwen-3 (14b), 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.

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