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Model: erwanf/gpt2-mini
Source: Original Platform

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README.md

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+---
+license: mit
+datasets:
+- Skylion007/openwebtext
+language:
+- en
+metrics:
+- perplexity
+pipeline_tag: text-generation
+---
+# GPT-2 Mini
+
+A smaller GPT-2 model with (only) 39M parameters. It was pretrained on a subset of OpenWebText, the open-source version of the pretraining dataset used by OpenAI for the original GPT-2 models.
+
+## Uses
+
+The purpose of this model is mainly for research and education. Its small size allows for fast experiments in resource-limited settings, while still being able of generating complex and coherent text.
+
+## Getting Started
+
+Use the code below to get started with the model:
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model
+model = AutoModelForCausalLM.from_pretrained("erwanf/gpt2-mini")
+model.eval()
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("erwanf/gpt2-mini")
+
+# Generate text
+prompt = "Hello, I'm a language model,"
+input_ids = tokenizer.encode(prompt, return_tensors="pt")
+
+output = model.generate(input_ids, do_sample=True, max_length=50, num_return_sequences=5)
+output_text = tokenizer.batch_decode(output, skip_special_tokens=True)
+print(output_text)
+```
+
+Output:
+```
+["Hello, I'm a language model, I can't be more efficient in words.\n\nYou can use this as a point to find out the next bit in your system, and learn more about me.\n\nI think a lot of the",
+ "Hello, I'm a language model, my teacher is a good teacher - a good school teacher – and one thing you have to remember:\n\nIt's not perfect. A school is not perfect; it isn't perfect at all!\n\n",
+ 'Hello, I\'m a language model, but if I can do something for you then go for it (for a word). Here is my blog, the language:\n\nI\'ve not used "normal" in English words, but I\'ve always',
+ 'Hello, I\'m a language model, I\'m talking to you the very first time I used a dictionary and it can be much better than one word in my dictionary. What would an "abnormal" English dictionary have to do with a dictionary and',
+ 'Hello, I\'m a language model, the most powerful representation of words and phrases in the language I\'m using."\n\nThe new rules change that makes it much harder for people to understand a language that does not have a native grammar (even with']
+```
+
+## Training Details
+
+The architecture relies on the GPT-2 model, with smaller dimensions and less layers. It uses the same tokenizer as GPT-2. We used the first 2M rows from the OpenWebText dataset, out of which we use 1k for test and validation sets.
+
+### Hyperparameters
+
+| **Hyperparameter**     | **Value**        |
+|------------------------|------------------|
+| **Model Parameters**   |                  |
+| Vocabulary Size        | 50,257           |
+| Context Length         | 512              |
+| Number of Layers       | 4                |
+| Hidden Size            | 512              |
+| Number of Attention Heads | 8             |
+| Intermediate Size      | 2048             |
+| Activation Function    | GELU             |
+| Dropout                | No               |
+| **Training Parameters**|                  |
+| Learning Rate          | 5e-4             |
+| Batch Size             | 256              |
+| Optimizer              | AdamW            |
+| beta1                  | 0.9              |
+| beta2                  | 0.98             |
+| Weight Decay           | 0.1              |
+| Training Steps         | 100,000          |
+| Warmup Steps           | 4,000            |
+| Learning Rate Scheduler| Cosine           |
+| Training Dataset Size  | 1M samples       |
+| Validation Dataset Size| 1k samples       |
+| Float Type             | bf16             |
+

config.json

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+{
+  "_name_or_path": "./outputs/gpt2_mini2/checkpoint-100000",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.0,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_embd": 512,
+  "n_head": 8,
+  "n_inner": 2048,
+  "n_layer": 4,
+  "n_positions": 512,
+  "pad_token_id": 50256,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.0,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "use_cache": true,
+  "vocab_size": 50257
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "eos_token_id": 50256,
+  "pad_token_id": 50256,
+  "transformers_version": "4.41.2"
+}

model.safetensors

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+oid sha256:16192f4149f7d6a7c754dbae4d0ec2d06a259b9b11b91445585ecb617f9b2bf0
+size 154422320

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "50256": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "model_max_length": 1024,
+  "pad_token": null,
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}