pragnyanramtha
/

pragnyan-clone

@@ -9,202 +9,125 @@ tags:
 - transformers
 - trl
 - unsloth
 ---
-# Model Card for Model ID
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-### Framework versions
-- PEFT 0.17.0

 - transformers
 - trl
 - unsloth
+license: mit
 ---
+## Model Card for Pragnyan-Clone-v1
+This is a personality-tuned version of Llama 3.1 8B, trained to mimic the conversational style, tone, and slang of Pragnyan Ramtha.
+It was fine-tuned on private chat logs using QLoRA and Unsloth to create a lightweight, highly efficient digital twin.Model DetailsModel Description
+This model is a LoRA (Low-Rank Adaptation) adapter fine-tuned on the unsloth/llama-3.1-8b-Instruct base model.
+It was trained to replicate a specific user's personality ("Pragnyan Ramtha") by learning from real-world conversation history (Instagram/WhatsApp).
+It captures nuances, casual sentence structure, and specific personal interests.
+The model was optimized for local deployment, trained on a cloud GPU (NVIDIA L4), and quantized to GGUF for efficient inference on consumer hardware via Ollama.
+## Developed by: Pragnyan Ramtha
+Model type: Causal Language Model (Fine-tuned Llama 3.1)
+Language(s) (NLP): English (with internet slang/informal syntax)
+Finetuned from model: unsloth/llama-3.1-8b-Instruct
+Model Sources
+Repository: [Link to your Hugging Face Repo]
+Base Model: Meta Llama 3.1 8B Instruct
+Tech Stack: Unsloth, TRL, PEFT, Ollama
 ## Uses
+Direct Use
+This model is intended for:
+Personality Simulation: Chatting with a digital clone of the creator.
+Style Transfer: Generating text in a specific, informal style.Local
+Chatbot: Running a highly responsive, personalized assistant on consumer GPUs (RTX 3060/4060).
+Out-of-Scope UseFactual Q&A: While based on Llama 3.1, this model is biased towards a specific personality's knowledge and may hallucinate facts to maintain character.
+Impersonation: This model should not be used to deceive others into thinking they are speaking to the real person.
+Bias, Risks, and Limitations
+Training Data Bias: The model reflects the opinions, biases, and language patterns found in the private chat logs used for training.
+Language Style: The model often uses informal language, slang, and non-standard grammar, which is a feature, not a bug.
+Hallucinations: Like all LLMs, it can generate confident but incorrect information.
+How to Get Started with the ModelYou can use this model directly with the peft and transformers library, or download the GGUF version for Ollama.Python Code (Adapter Only)
+```
+from unsloth import FastLanguageModel
+from peft import PeftModel
+# 1. Load Base Model
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "unsloth/llama-3.1-8b-Instruct",
+    max_seq_length = 8192,
+    dtype = None,
+    load_in_4bit = True,
+)
+# 2. Load Adapters
+model = PeftModel.from_pretrained(model, "PragnyanRamtha/pragnyan-clone-v1")
+# 3. Inference
+FastLanguageModel.for_inference(model)
+messages = [
+    {"role": "user", "content": "Yo, what's good?"},
+]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
+outputs = model.generate(inputs, max_new_tokens=64, use_cache=True)
+print(tokenizer.batch_decode(outputs))
+```
+Local Use (Ollama)Download the .gguf file from the "Files" tab and create a Modelfile:
+```
+FROM ./pragnyan-clone-v1.q4_k_m.gguf
+TEMPLATE """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+{{ .System }}<|eot_id|><|start_header_id|>user<|end_header_id|>
+{{ .Prompt }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+{{ .Response }}<|eot_id|>"""
+SYSTEM """You are Pragnyan Ramtha."""
+```
+Training Procedure
+Size: ~13,500 training examples.
+The model was fine-tuned using Unsloth for 2x faster training and optimized VRAM usage.
+We used QLoRA (Quantized Low-Rank Adaptation) to train on a single GPU.
+### Training Hyperparameters
+Training regime: 4-bit QLoRA (bfloat16 precision)
+Optimizer: paged_adamw_8bit (Paged AdamW to save memory)
+Learning Rate: 2e-4
+Epochs: 1
+Batch Size: 2 (per device)
+Gradient Accumulation: 8 (Effective batch size = 16)
+LoRA Rank (r): 32.
+LoRA Alpha: 64LoRA
+Dropout: 0.05Max
+Sequence Length: 8192
+Hardware: NVIDIA L4 GPU (24GB VRAM) on Google Cloud.
+Training Time: ~1.5 hours for 1 epoch.
+VRAM Usage: Peaked at ~16GB during training.
+EvaluationResultsFinal Validation Loss: ~1.14
+Qualitative Eval: The model successfully adopts the target persona, maintaining conversation flow without breaking character.