Push model using huggingface_hub.
Browse files- README.md +42 -0
- adapter_config.json +16 -0
- adapter_model.bin +3 -0
- pytorch_model.bin +3 -0
README.md
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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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# TRL Model
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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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## Usage
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To use this model for inference, first install the TRL library:
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```bash
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python -m pip install trl
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```
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You can then generate text as follows:
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```python
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from transformers import pipeline
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generator = pipeline("text-generation", model="eurus7//tmp/tmppczifg03/eurus7/working")
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outputs = generator("Hello, my llama is cute")
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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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```python
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from transformers import AutoTokenizer
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from trl import AutoModelForCausalLMWithValueHead
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tokenizer = AutoTokenizer.from_pretrained("eurus7//tmp/tmppczifg03/eurus7/working")
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model = AutoModelForCausalLMWithValueHead.from_pretrained("eurus7//tmp/tmppczifg03/eurus7/working")
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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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adapter_config.json
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{
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"base_model_name_or_path": "lvwerra/gpt2-imdb",
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"bias": "none",
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"fan_in_fan_out": true,
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"inference_mode": true,
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"init_lora_weights": true,
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"lora_alpha": 32,
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"lora_dropout": 0.05,
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 16,
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"target_modules": [
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"c_attn"
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],
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"task_type": "CAUSAL_LM"
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}
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adapter_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:9000d502320b131c652d85ad69a3aa845bbd99f2136348aed7c54bfe3fec2b5a
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size 2367673
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:c0b2247ee89ff3ec4430ef3b0c7023589f9fc12a35082eb4beb9eb4ec302ad34
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size 4159
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