Update README.md

README.md

@@ -9,202 +9,125 @@ tags:
 - transformers
 - trl
 - unsloth
+license: mit
 ---
+## Model Card for Pragnyan-Clone-v1
 
-# Model Card for Model ID
+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
 
-<!-- Provide a quick summary of what the model is/does. -->
+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)
 
-## Model Details
+Finetuned from model: unsloth/llama-3.1-8b-Instruct
 
-### Model Description
+Model Sources
 
-<!-- Provide a longer summary of what this model is. -->
+Repository: [Link to your Hugging Face Repo]
 
+Base Model: Meta Llama 3.1 8B Instruct
 
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+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)
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
+```
+from unsloth import FastLanguageModel
 
-<!-- Relevant interpretability work for the model goes here -->
 
-[More Information Needed]
+from peft import PeftModel
 
-## Environmental Impact
+# 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,
+)
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+# 2. Load Adapters
+model = PeftModel.from_pretrained(model, "PragnyanRamtha/pragnyan-clone-v1")
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+# 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")
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+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
 
-## Technical Specifications [optional]
+TEMPLATE """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
 
-### Model Architecture and Objective
+{{ .System }}<|eot_id|><|start_header_id|>user<|end_header_id|>
 
-[More Information Needed]
+{{ .Prompt }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
 
-### Compute Infrastructure
+{{ .Response }}<|eot_id|>"""
+SYSTEM """You are Pragnyan Ramtha."""
+```
 
-[More Information Needed]
 
-#### Hardware
+Training Procedure
 
-[More Information Needed]
+Size: ~13,500 training examples.
 
-#### Software
+The model was fine-tuned using Unsloth for 2x faster training and optimized VRAM usage.
 
-[More Information Needed]
+We used QLoRA (Quantized Low-Rank Adaptation) to train on a single GPU.
 
-## Citation [optional]
+### Training Hyperparameters
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+Training regime: 4-bit QLoRA (bfloat16 precision)
 
-**BibTeX:**
+Optimizer: paged_adamw_8bit (Paged AdamW to save memory)
 
-[More Information Needed]
+Learning Rate: 2e-4
 
-**APA:**
+Epochs: 1
 
-[More Information Needed]
+Batch Size: 2 (per device)
 
-## Glossary [optional]
+Gradient Accumulation: 8 (Effective batch size = 16)
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+LoRA Rank (r): 32.
 
-[More Information Needed]
+LoRA Alpha: 64LoRA 
 
-## More Information [optional]
+Dropout: 0.05Max 
 
-[More Information Needed]
+Sequence Length: 8192
 
-## Model Card Authors [optional]
+Hardware: NVIDIA L4 GPU (24GB VRAM) on Google Cloud.
 
-[More Information Needed]
+Training Time: ~1.5 hours for 1 epoch.
 
-## Model Card Contact
+VRAM Usage: Peaked at ~16GB during training.
 
-[More Information Needed]
-### Framework versions
+EvaluationResultsFinal Validation Loss: ~1.14
 
-- PEFT 0.17.0
+Qualitative Eval: The model successfully adopts the target persona, maintaining conversation flow without breaking character.