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README.md

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+---
+base_model: segestic/Tinystories-gpt-0.1-3m
+datasets:
+- roneneldan/TinyStories
+inference: true
+language:
+- en
+library_name: transformers
+model_creator: segestic
+model_name: Tinystories-gpt-0.1-3m
+pipeline_tag: text-generation
+quantized_by: afrideva
+tags:
+- gguf
+- ggml
+- quantized
+---
+
+# Tinystories-gpt-0.1-3m-GGUF
+
+Quantized GGUF model files for [Tinystories-gpt-0.1-3m](https://huggingface.co/segestic/Tinystories-gpt-0.1-3m) from [segestic](https://huggingface.co/segestic)
+
+## Original Model Card:
+
+## We tried to use the huggingface transformers library to recreate the TinyStories models on Consumer GPU using GPT2 Architecture instead of GPT-Neo Architecture orignally used in the paper (https://arxiv.org/abs/2305.07759). Output model is 15mb and has 3 million parameters. 
+
+
+
+# ------ EXAMPLE USAGE 1 ---
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("segestic/Tinystories-gpt-0.1-3m")
+
+model = AutoModelForCausalLM.from_pretrained("segestic/Tinystories-gpt-0.1-3m")
+
+prompt = "Once upon a time there was"
+
+input_ids = tokenizer.encode(prompt, return_tensors="pt")
+#### Generate completion
+output = model.generate(input_ids, max_length = 1000, num_beams=1)
+#### Decode the completion
+output_text = tokenizer.decode(output[0], skip_special_tokens=True)
+#### Print the generated text
+print(output_text)
+
+
+
+# ------ EXAMPLE USAGE 2 ------
+## Use a pipeline as a high-level helper
+from transformers import pipeline
+#### pipeline
+pipe = pipeline("text-generation", model="segestic/Tinystories-gpt-0.1-3m")
+#### prompt
+prompt = "where is the little girl"
+#### generate completion
+output = pipe(prompt, max_length=1000, num_beams=1)
+#### decode the completion
+generated_text = output[0]['generated_text']
+#### Print the generated text
+print(generated_text)