SummerSigh commited on
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Push model using huggingface_hub.

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README.md ADDED
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+ ---
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+ license: apache-2.0
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+ tags:
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+ - trl
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+ - transformers
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+ - reinforcement-learning
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+ ---
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+
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+ # TRL Model
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+
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+ This is a [TRL language model](https://github.com/lvwerra/trl) that has been fine-tuned with reinforcement learning to
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+ guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
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+
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+ ## Usage
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+
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+ To use this model for inference, first install the TRL library:
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+
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+ ```bash
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+ python -m pip install trl
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+ ```
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+
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+ You can then generate text as follows:
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+
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+ ```python
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+ from transformers import pipeline
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+
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+ generator = pipeline("text-generation", model="SummerSigh//tmp/tmpn8nky8x7/SummerSigh/T5-Base-EvilPrompterRM")
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+ outputs = generator("Hello, my llama is cute")
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+ ```
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+
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+ If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
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+
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+ ```python
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+ from transformers import AutoTokenizer
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+ from trl import AutoModelForCausalLMWithValueHead
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+
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+ tokenizer = AutoTokenizer.from_pretrained("SummerSigh//tmp/tmpn8nky8x7/SummerSigh/T5-Base-EvilPrompterRM")
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+ model = AutoModelForCausalLMWithValueHead.from_pretrained("SummerSigh//tmp/tmpn8nky8x7/SummerSigh/T5-Base-EvilPrompterRM")
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+
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+ inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
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+ outputs = model(**inputs, labels=inputs["input_ids"])
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+ ```
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+ "_name_or_path": "SummerSigh/t5-ROTLabel-to-Prompt",
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+ "architectures": [
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+ "T5ForConditionalGeneration"
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+ ],
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+ "dense_act_fn": "gelu_new",
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+ "dropout_rate": 0.1,
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+ "eos_token_id": 1,
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+ "feed_forward_proj": "gated-gelu",
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+ "initializer_factor": 1.0,
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+ "is_encoder_decoder": true,
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+ "is_gated_act": true,
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+ "layer_norm_epsilon": 1e-06,
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+ "model_type": "t5",
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+ "n_positions": 512,
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+ "num_decoder_layers": 12,
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+ "num_heads": 12,
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+ "num_layers": 12,
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+ "output_past": true,
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+ "relative_attention_max_distance": 128,
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+ "relative_attention_num_buckets": 32,
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+ "task_specific_params": {
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+ "summarization": {
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+ "early_stopping": true,
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+ "length_penalty": 2.0,
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+ },
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+ "translation_en_to_de": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to German: "
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+ },
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+ "translation_en_to_fr": {
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+ "early_stopping": true,
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+ "max_length": 300,
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+ "num_beams": 4,
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+ "prefix": "translate English to French: "
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+ },
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+ "translation_en_to_ro": {
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+ "early_stopping": true,
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+ "num_beams": 4,
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+ "prefix": "translate English to Romanian: "
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+ }
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+ },
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "float32",
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+ "transformers_version": "4.26.1",
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+ "use_cache": true,
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+ "vocab_size": 32128
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+ }
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+ }
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