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Model: arif-butt/tinyllama-trl-merged
Source: Original Platform

README.md

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+---
+language: en
+license: apache-2.0
+library_name: transformers
+tags:
+- tinyllama
+- trl
+- merged
+- lora
+- fine-tuned
+- pytorch
+- causal-lm
+- text-generation
+- conversational
+base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+datasets:
+- arif-butt/arifbutt_dataset
+pipeline_tag: text-generation
+inference: false
+---
+
+# 🦙 TinyLlama TRL Merged - Complete Fine-tuned Model
+
+## 📋 Model Overview
+
+This is a **fully merged and standalone model** of TinyLlama (1.1B parameters) fine-tuned using **TRL (Transformer Reinforcement Learning)** framework with LoRA adapters. The LoRA weights have been permanently merged into the base model, creating a single complete model that can be loaded without any adapter libraries.
+
+### Key Features
+| Feature | Description |
+|---------|-------------|
+| **Standalone** | No PEFT library required — single model file |
+| **Fine-tuned** | Custom trained on educational Q&A dataset |
+| **Optimized** | FP16 precision for memory efficiency |
+| **Production Ready** | Single folder deployment |
+| **Chat Optimized** | Fine-tuned for conversational responses |
+
+### Model Architecture
+| Component | Specification |
+|-----------|---------------|
+| **Base Model** | TinyLlama-1.1B-Chat-v1.0 |
+| **Architecture** | Llama-based transformer decoder |
+| **Total Parameters** | 1,100,000,000 (1.1B) |
+| **Context Length** | 2048 tokens |
+| **Hidden Size** | 2048 |
+| **Intermediate Size** | 5632 |
+| **Number of Layers** | 22 |
+| **Number of Attention Heads** | 32 |
+| **Number of Key/Value Heads** | 4 (GQA) |
+| **Head Dimension** | 64 |
+| **Activation Function** | SwiGLU |
+| **Positional Encoding** | RoPE (Rotary Position Embedding) |
+| **Attention Mechanism** | Grouped-Query Attention (GQA) |
+| **Precision** | FP16 (float16) |
+
+## 🚀 Usage Guide
+
+### Installation
+```bash
+pip install transformers torch accelerate
+
+Method 1: Direct Transformers Loading
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Load model and tokenizer
+model_id = "arif-butt/tinyllama-trl-merged"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+model.eval()
+
+# Define prompt template
+prompt = "Q: What is machine learning?\nA:"
+
+# Tokenize
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+# Generate
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=150,
+        temperature=0.7,
+        top_p=0.95,
+        do_sample=True,
+        repetition_penalty=1.1,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+
+# Decode and print
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(f"Prompt: {prompt}")
+print(f"Response: {response[len(prompt):].strip()}")
+
+Method 2: Pipeline for Simple Inference
+from transformers import pipeline
+
+pipe = pipeline(
+    "text-generation",
+    model="arif-butt/tinyllama-trl-merged",
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+prompt = "Q: Explain neural networks in simple terms\nA:"
+result = pipe(prompt, max_new_tokens=150, temperature=0.7, do_sample=True)
+print(result[0]["generated_text"])
+