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Model: lgaalves/gpt2_guanaco-dolly-platypus
Source: Original Platform

.gitattributes

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README.md

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+---
+license: mit
+datasets:
+- garage-bAInd/Open-Platypus
+- databricks/databricks-dolly-15k
+- timdettmers/openassistant-guanaco
+language:
+- en
+pipeline_tag: text-generation
+---
+
+# gpt2_guanaco-dolly-platypus
+
+**gpt2_guanaco-dolly-platypus** is an instruction fine-tuned model based on the GPT-2 transformer architecture.
+
+
+### Benchmark Metrics
+
+| Metric                | gpt2_guanaco-dolly-platypus | GPT-2 (base) |
+|-----------------------|-------|-------|
+| Avg.                  | **30.25** | 29.9 |
+| ARC (25-shot)         | **23.55** | 21.84 |
+| HellaSwag (10-shot)   | 31.03 | **31.6** |
+| MMLU (5-shot)         | **26.4** | 25.86 |
+| TruthfulQA (0-shot)   | 40.02 |  **40.67**  |
+
+
+We use state-of-the-art [Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness) to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard. Please see below for detailed instructions on reproducing benchmark results.
+
+### Model Details
+
+* **Trained by**: Luiz G A Alves
+* **Model type:**  **gpt2_guanaco-dolly-platypus** is an auto-regressive language model based on the GPT-2 transformer architecture.
+* **Language(s)**: English
+
+### How to use:
+
+```python
+# Use a pipeline as a high-level helper
+>>> from transformers import pipeline
+>>> pipe = pipeline("text-generation", model="lgaalves/gpt2_guanaco-dolly-platypus")
+>>> question = "What is a large language model?"
+>>> answer = pipe(question)
+>>> print(answer[0]['generated_text'])
+```
+
+or, you can load the model direclty using:
+
+```python
+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("lgaalves/gpt2_open-platypus")
+model = AutoModelForCausalLM.from_pretrained("lgaalves/gpt2_open-platypus")
+```
+
+### Training Dataset
+
+`lgaalves/gpt2_guanaco-dolly-platypus` was trained using 3 datasets:
+ - [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco)
+ - [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k)
+ - [garage-bAInd/Open-Platypus](https://huggingface.co/datasets/garage-bAInd/Open-Platypus)
+
+### Training Procedure
+
+`lgaalves/gpt2_guanaco-dolly-platypus` was instruction fine-tuned using LoRA on 1 T4 GPU on Google Colab. It took about 1 hour to train it.  
+
+
+# Intended uses, limitations & biases
+
+You can use the raw model for text generation or fine-tune it to a downstream task. The model was not extensively tested and may produce false information. It contains a lot of unfiltered content from the internet, which is far from neutral.
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lgaalves__gpt2_guanaco-dolly-platypus)
+
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 25.15   |
+| ARC (25-shot)         | 23.55          |
+| HellaSwag (10-shot)   | 31.03    |
+| MMLU (5-shot)         | 26.4         |
+| TruthfulQA (0-shot)   | 40.02   |
+| Winogrande (5-shot)   | 50.12   |
+| GSM8K (5-shot)        | 0.0        |
+| DROP (3-shot)         | 4.96         |

config.json

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+{
+  "_name_or_path": "gpt2",
+  "activation_function": "gelu_new",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "do_sample": true,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.02,
+  "layer_norm_epsilon": 1e-05,
+  "max_length": 50,
+  "model_type": "gpt2",
+  "n_ctx": 1024,
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": null,
+  "n_layer": 12,
+  "n_positions": 1024,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "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,
+  "task_specific_params": {
+    "text-generation": {
+      "do_sample": true,
+      "max_length": 50
+    }
+  },
+  "torch_dtype": "float16",
+  "transformers_version": "4.31.0",
+  "use_cache": true,
+  "vocab_size": 50257
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 50256,
+  "do_sample": true,
+  "eos_token_id": 50256,
+  "max_length": 50,
+  "transformers_version": "4.31.0"
+}

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special_tokens_map.json

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+{
+  "bos_token": "<|endoftext|>",
+  "eos_token": "<|endoftext|>",
+  "pad_token": "<|endoftext|>",
+  "unk_token": "<|endoftext|>"
+}

tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "bos_token": "<|endoftext|>",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|endoftext|>",
+  "model_max_length": 1024,
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "<|endoftext|>"
+}