Instructions to use mrplexar/PortfolioLLM-RAG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrplexar/PortfolioLLM-RAG with Transformers:

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

pipe = pipeline("text-generation", model="mrplexar/PortfolioLLM-RAG")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrplexar/PortfolioLLM-RAG")
model = AutoModelForCausalLM.from_pretrained("mrplexar/PortfolioLLM-RAG", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrplexar/PortfolioLLM-RAG with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mrplexar/PortfolioLLM-RAG

SGLang

How to use mrplexar/PortfolioLLM-RAG 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 "mrplexar/PortfolioLLM-RAG" \
    --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": "mrplexar/PortfolioLLM-RAG",
		"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 "mrplexar/PortfolioLLM-RAG" \
        --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": "mrplexar/PortfolioLLM-RAG",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mrplexar/PortfolioLLM-RAG with Docker Model Runner:
```
docker model run hf.co/mrplexar/PortfolioLLM-RAG
```

mrplexar commited on Apr 10

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+---
+base_model: HuggingFaceTB/SmolLM2-360M-Instruct
+library_name: peft
+license: apache-2.0
+tags:
+- portfolio
+- conversational
+- qlora
+- smollm2
+- personal-agent
+- text-generation
+- custom-persona
+language:
+- en
+---
+# Arpit Ramesan's Portfolio AI Agent (SmolLM2-360M)
+## Model Description
+This is a custom-trained conversational AI agent designed to act as the personal representative for **Arpit Ramesan**, an AI/ML Engineer and aspiring AI Generalist based in Thalassery, Kerala.
+The model was fine-tuned using QLoRA on a synthetic dataset generated specifically from Arpit's real-world portfolio, resume, and technical blogs. It is optimized to answer questions regarding his career timeline, technical architecture, and creative hubs, using a professional yet conversational tone.
+- **Developed by:** Arpit Ramesan (@mrplexar)
+- **Model type:** Causal Language Model (Fine-tuned via PEFT/LoRA and merged)
+- **Base model:** `HuggingFaceTB/SmolLM2-360M-Instruct`
+- **Language(s):** English (with internet slang/casual conversational capabilities)
+- **License:** Apache 2.0
+## Intended Uses & Limitations
+### Intended Use
+This model is specifically designed to be deployed as the backend inference engine for a web-based portfolio OS. It expects a strict ChatML-style prompt structure and a specific system prompt to reliably recall its training.
+### Limitations
+- **Scope Context:** It is a "Micro-LLM" (360M parameters). While highly efficient, it is strictly domain-specific. Asking it general knowledge questions outside the scope of Arpit's portfolio may result in hallucinations or base-model bleed-through.
+- **Prompt Sensitivity:** The model requires greedy decoding (`temperature=0.01`, `do_sample=False`) and the exact system prompt from its training to avoid identity drift.
+## How to Get Started with the Model
+Use the code below to run inference using the Hugging Face `InferenceClient`, which requires zero local VRAM and is optimized for lightweight web servers like PythonAnywhere.
+```python
+from huggingface_hub import InferenceClient
+REPO_ID = "mrplexar/PortfolioLLM-RAG"
+HF_TOKEN = "your_hf_read_token_here"
+client = InferenceClient(model=REPO_ID, token=HF_TOKEN)
+SYSTEM_PROMPT = "You are the AI agent for Arpit Ramesan's portfolio. You answer questions about his career, creative projects, and technical skills using a professional, personal, and conversational tone. You know that Arpit is based in Thalassery, Kerala."
+user_msg = "What kind of projects does Arpit build?"
+# Apply exact training template
+prompt = f"<|im_start|>user\n{SYSTEM_PROMPT}\n\n{user_msg}<|im_end|>\n<|im_start|>assistant\n"
+reply = client.text_generation(
+    prompt,
+    max_new_tokens=150,
+    temperature=0.01,
+    stop_sequences=["<|im_end|>"]
+)
+print(reply.strip())
+```
+## Training Details
+### Training Data
+The model was fine-tuned on a custom JSONL dataset containing 150+ diverse, synthetic Q&A pairs. The dataset was generated using an automated pipeline powered by gemma4:e2b.
+The data encompasses:
+Personal Biometrics & Timeline (Education at St. Thomas College of Engineering, ML Engineer at Incramania, Intern Lead at Saspo World Tech).
+Technical Projects (WhatsApp DDoS Defense, Theyyam Detection System, Brain Disease Detection).
+Live Deployments & Web Architecture.
+Creative Hubs (YouTube, Spotify Podcasts, Pinterest).
+Conversational augmentations (internet slang, casual formatting, varied query lengths).
+## Training Procedure
+The model was trained using QLoRA (Quantized Low-Rank Adaptation) to accommodate hardware constraints (NVIDIA GTX 1650 4GB VRAM) while maximizing parameter updates. The LoRA adapters were subsequently fused into the base model weights for seamless API deployment.
+## Training Hyperparameters
+- **Precision:** 4-bit (nf4) quantization during training, upcast to float16.
+- **Batch Size:** 1 (with Gradient Accumulation steps = 8).
+- **Optimizer:** paged_adamw_32bit.
+- **Learning Rate:** 2e-4 with a cosine learning rate scheduler.
+- **Max Steps:** 200
+- **Target Modules:** ["q_proj", "o_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"]
+- **LoRA Rank (r):** 16
+- **LoRA Alpha:** 32
+## Hardware & Environment
+Hardware Type: NVIDIA GTX 1650 (Mobile)
+VRAM Required: ~3.2 GB during QLoRA training
+Frameworks: transformers, peft, trl, bitsandbytes, torch