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

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
```

Rustamshry commited on Feb 19

Commit

767eeab

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1 Parent(s): d741fc9

Update README.md

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@@ -15,7 +15,7 @@ pipeline_tag: text-generation
 library_name: peft
 ---
-# Uploaded  model
 - **Developed by:** khazarai
 - **License:** apache-2.0
@@ -23,4 +23,54 @@ library_name: peft
 This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 library_name: peft
 ---
+# Model Description
 - **Developed by:** khazarai
 - **License:** apache-2.0
 This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/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.