Instructions to use khazarai/Med-R1-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use khazarai/Med-R1-14B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen3-14b-unsloth-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "khazarai/Med-R1-14B")

Transformers

How to use khazarai/Med-R1-14B with Transformers:

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

pipe = pipeline("text-generation", model="khazarai/Med-R1-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("khazarai/Med-R1-14B", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use khazarai/Med-R1-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "khazarai/Med-R1-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "khazarai/Med-R1-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/khazarai/Med-R1-14B

SGLang

How to use khazarai/Med-R1-14B 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 "khazarai/Med-R1-14B" \
    --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": "khazarai/Med-R1-14B",
		"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 "khazarai/Med-R1-14B" \
        --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": "khazarai/Med-R1-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use khazarai/Med-R1-14B 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 khazarai/Med-R1-14B 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 khazarai/Med-R1-14B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for khazarai/Med-R1-14B to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="khazarai/Med-R1-14B",
    max_seq_length=2048,
)

Docker Model Runner
How to use khazarai/Med-R1-14B with Docker Model Runner:
```
docker model run hf.co/khazarai/Med-R1-14B
```

Model Description

Developed by: khazarai
License: apache-2.0
Finetuned from model : unsloth/qwen3-14b-unsloth-bnb-4bit

This qwen3 model was trained 2x faster with Unsloth

This model is a QLoRA fine-tuned version of unsloth/qwen3-14b-unsloth-bnb-4bit, originally based on the Qwen3-14B architecture developed by the Qwen Team. The model has been fine-tuned on the Chain of Thought – Heartbreak & Breakups Dataset (MIT Licensed), consisting of 9.8k high-quality Q&A pairs focused on emotional processing, coping strategies, and relationship dynamics following breakups. The goal of this fine-tuning is to enhance:

Emotional reasoning capability
Structured chain-of-thought generation
Empathetic and psychologically grounded responses
Relationship pattern analysis
Identity reconstruction & self-esteem rebuilding guidance

🧠 Base Model

Base architecture: Qwen3-14B
Variant: unsloth/qwen3-14b-unsloth-bnb-4bit
Quantization: 4-bit (bitsandbytes)
Fine-tuning method: QLoRA
Adapter type: LoRA
Training precision: 4-bit base + 16-bit adapters

🎯 Intended Use

This model is intended for:

Mental health–adjacent AI assistants
Relationship guidance systems
Emotional reasoning research
Chain-of-thought alignment experiments
NLP research on structured reasoning in affective domains

The model aims to produce:

Step-by-step reasoning
Balanced perspectives
Reduced reactive or extreme advice

⚠️ Limitations

Not a substitute for licensed therapy
May generate plausible but non-clinically validated advice
Trained on synthetic / curated emotional scenarios
Chain-of-thought exposure may increase verbosity
Emotional nuance outside breakup domain may be limited

This model should not be used for crisis intervention or high-risk mental health scenarios.

How to get started with Model

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel


tokenizer = AutoTokenizer.from_pretrained("unsloth/qwen3-14b-unsloth-bnb-4bit")
base_model = AutoModelForCausalLM.from_pretrained(
    "unsloth/qwen3-14b-unsloth-bnb-4bit",
    device_map={"": 0}
)

model = PeftModel.from_pretrained(base_model,"khazarai/Med-R1-14B")

question = """
How can someone work through and move past deeply painful memories associated with trauma, understanding that "moving past" doesn't mean forgetting but rather integrating the experience in a healthy way?
"""

messages = [
    {"role" : "user", "content" : question}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize = False,
    add_generation_prompt = True,
    enable_thinking = True,
)

from transformers import TextStreamer
_ = model.generate(
    **tokenizer(text, return_tensors = "pt").to("cuda"),
    max_new_tokens = 2048,
    temperature = 0.6,
    top_p = 0.95,
    top_k = 20,
    streamer = TextStreamer(tokenizer, skip_prompt = True),
)

🧪 Future Work

Domain expansion to broader emotional intelligence tasks
Controlled reasoning output (hidden CoT vs visible CoT)
Evaluation via human annotation
Cross-cultural emotional adaptation

Downloads last month: 2

Dataset used to train khazarai/Med-R1-14B

Collection including khazarai/Med-R1-14B

Reasoning Models

Collection

Chain of Thought • 11 items • Updated Apr 16 • 2