102 lines
2.8 KiB
Markdown
102 lines
2.8 KiB
Markdown
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---
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language: en
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license: apache-2.0
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library_name: transformers
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tags:
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- tinyllama
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- peft
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- merged
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- lora
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- fine-tuned
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- pytorch
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- text-generation
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base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
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datasets:
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- arif-butt/arifbutt_dataset
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pipeline_tag: text-generation
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---
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# 🦙 TinyLlama PEFT Merged
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> **A fully merged, production-ready TinyLlama model fine-tuned with PEFT LoRA**
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---
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## 📌 Quick Facts
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| Attribute | Value |
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|-----------|-------|
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| **Model Size** | 2.2 GB |
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| **Parameters** | 1.1 Billion |
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| **Format** | PyTorch Safetensors |
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| **Precision** | FP16 |
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| **Context** | 2048 tokens |
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| **Training Framework** | PEFT + TRL |
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| **Inference** | No PEFT required |
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---
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## 🚀 Quick Start
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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# One-liner to load
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tokenizer = AutoTokenizer.from_pretrained("arif-butt/tinyllama-peft-merged")
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model = AutoModelForCausalLM.from_pretrained(
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"arif-butt/tinyllama-peft-merged",
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torch_dtype=torch.float16,
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device_map="auto"
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)
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# Generate
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prompt = "Q: What courses does Arif teach?\nA:"
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=100)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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📦 What's Inside
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tinyllama-peft-merged/
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├── model.safetensors # 2.2 GB — merged weights
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├── config.json # Model architecture
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├── generation_config.json # Default generation settings
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├── tokenizer.json # Vocabulary (1.76 MB)
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├── tokenizer_config.json # Tokenizer settings
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└── special_tokens_map.json # Special tokens
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No adapter files. No PEFT needed. Just load and go.
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🔧 Generation Settings
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outputs = model.generate(
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**inputs,
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max_new_tokens=150,
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temperature=0.7,
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top_p=0.95,
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do_sample=True,
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repetition_penalty=1.1,
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pad_token_id=tokenizer.eos_token_id,
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)
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💬 Prompt Format
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Q: Your question here?
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A:
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Example:
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Q: What is deep learning?
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A: Deep learning is a subset of machine learning...
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Q: What is Python?
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A: Python is a high-level, interpreted programming language known for its simple, readable syntax. It supports multiple programming paradigms including object-oriented, imperative, and functional programming.
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Q: Explain gradient descent
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A: Gradient descent is an optimization algorithm used to minimize the loss function in machine learning models. It works by iteratively moving parameters in the direction of the negative gradient.
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Q: Name Arif's courses
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A: Dr. Muhammad Arif Butt teaches Python Programming, Data Structures & Algorithms, Machine Learning, and Deep Learning courses.
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Epoch 1: ████████████████░░░░ 0.8
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Epoch 2: ████████████████████ 0.4
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Epoch 3: ████████████████████ 0.05
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