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Model: souvik18/Roy-v1 Source: Original Platform
2026-08-27 09:59:17 +08:00
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+---
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- mistral
+- chatbot
+---
+#  Roy-v1
+
+**Roy** is a personal AI assistant model created and fine-tuned by **Souvik Pramanick**.  
+Designed to be helpful, conversational, and practical for everyday tasks such as learning, coding, problem solving, and general assistance.
+
+---
+
+## Creator
+
+**Founder & Trainer:**  
+ **Souvik Pramanick**  
+GitHub: https://github.com/Souvik18p  
+HuggingFace: https://huggingface.co/souvik18
+
+Roy is an independent project built with the vision of creating a smart, friendly, and customizable AI assistant.
+
+---
+
+##  What Roy Can Do
+
+Roy is capable of:
+
+-  Natural conversation and assistance  
+-  Answering general knowledge questions  
+-  Solving math and logical problems  
+-  Helping with coding and debugging  
+-  Writing emails, stories, and content  
+-  Explaining concepts in simple language  
+-  Brainstorming ideas and learning support
+
+---
+
+##  Model Details
+
+- **Model Name:** Roy-v1  
+- **Parameters:** 7B  
+- **Architecture:** LLaMA-based  
+- **Tensor Type:** F16  
+- **Format:** Safetensors  
+- **License:** Open for community usage
+
+Base Model: **souvik18/Roy-v1**
+
+## Quantized Versions (Community)
+
+Thanks to [@mradermacher](https://huggingface.co/mradermacher) for providing GGUF quants of Roy-v1:
+
+**https://huggingface.co/mradermacher/Roy-v1-GGUF**
+
+These versions allow Roy to run on:
+- CPU only systems  
+- Low VRAM GPUs  
+- Mobile / local apps via llama.cpp, ollama, koboldcpp
+
+
+## Quick Usage
+
+### Using HuggingFace Transformers
+
+```python
+!pip install -U transformers datasets accelerate bitsandbytes peft huggingface_hub
+
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import torch
+
+MODEL_ID = "souvik18/Roy-v1"
+
+# 4bit config – works best on Kaggle
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_use_double_quant=True,
+)
+
+print(" Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+tokenizer.pad_token = tokenizer.eos_token
+
+print(" Loading model (4bit)...")
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    quantization_config=bnb_config,
+    device_map="auto"
+)
+
+print("\n Roy-v1 Loaded Successfully!")
+
+while True:
+    text = input("You: ")
+    if text.lower() in ["exit","quit"]:
+        break
+
+    prompt = f"[INST] {text} [/INST]"
+
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    with torch.no_grad():
+        out = model.generate(
+            **inputs,
+            max_new_tokens=200,
+            temperature=0.7,
+            top_p=0.9,
+            do_sample=True
+        )
+
+
+