Cleanup: Batch removing all files before update

.gitattributes

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README.md

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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

chat_template.jinja

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-{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
-You are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>
-' }}{% endif %}{{'<|im_start|>' + message['role'] + '
-' + message['content'] + '<|im_end|>' + '
-'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
-' }}{% endif %}

config.json

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-{
-  "architectures": [
-    "LlamaForCausalLM"
-  ],
-  "attention_bias": false,
-  "attention_dropout": 0.0,
-  "bos_token_id": 1,
-  "dtype": "float32",
-  "eos_token_id": 2,
-  "head_dim": 64,
-  "hidden_act": "silu",
-  "hidden_size": 960,
-  "initializer_range": 0.02,
-  "intermediate_size": 2560,
-  "is_llama_config": true,
-  "max_position_embeddings": 8192,
-  "mlp_bias": false,
-  "model_type": "llama",
-  "num_attention_heads": 15,
-  "num_hidden_layers": 32,
-  "num_key_value_heads": 5,
-  "pad_token_id": 2,
-  "pretraining_tp": 1,
-  "rms_norm_eps": 1e-05,
-  "rope_interleaved": false,
-  "rope_parameters": {
-    "rope_theta": 100000,
-    "rope_type": "default"
-  },
-  "tie_word_embeddings": true,
-  "transformers.js_config": {
-    "kv_cache_dtype": {
-      "fp16": "float16",
-      "q4f16": "float16"
-    }
-  },
-  "transformers_version": "5.4.0",
-  "use_cache": true,
-  "vocab_size": 49152
-}

generation_config.json

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-{
-  "_from_model_config": true,
-  "bos_token_id": 1,
-  "eos_token_id": 2,
-  "pad_token_id": 2,
-  "transformers_version": "5.4.0"
-}

model.safetensors

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tokenizer_config.json

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-{
-  "add_prefix_space": false,
-  "backend": "tokenizers",
-  "bos_token": "<|im_start|>",
-  "clean_up_tokenization_spaces": false,
-  "eos_token": "<|im_end|>",
-  "errors": "replace",
-  "extra_special_tokens": [
-    "<|im_start|>",
-    "<|im_end|>"
-  ],
-  "is_local": false,
-  "model_max_length": 8192,
-  "pad_token": "<|im_end|>",
-  "tokenizer_class": "GPT2Tokenizer",
-  "unk_token": "<|endoftext|>",
-  "vocab_size": 49152
-}