Push model using huggingface_hub.

README.md

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+---
+license: apache-2.0
+tags:
+- trl
+- transformers
+- reinforcement-learning
+---
+
+# TRL Model
+
+This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
+ guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
+
+## Usage
+
+To use this model for inference, first install the TRL library:
+
+```bash
+python -m pip install trl
+```
+
+You can then generate text as follows:
+
+```python
+from transformers import pipeline
+
+generator = pipeline("text-generation", model="amirabdullah19852020//tmp/tmpc90q8o6q/amirabdullah19852020/gpt-j-6b-sharded-bf16_sentiment_reward")
+outputs = generator("Hello, my llama is cute")
+```
+
+If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
+
+```python
+from transformers import AutoTokenizer
+from trl import AutoModelForCausalLMWithValueHead
+
+tokenizer = AutoTokenizer.from_pretrained("amirabdullah19852020//tmp/tmpc90q8o6q/amirabdullah19852020/gpt-j-6b-sharded-bf16_sentiment_reward")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("amirabdullah19852020//tmp/tmpc90q8o6q/amirabdullah19852020/gpt-j-6b-sharded-bf16_sentiment_reward")
+
+inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
+outputs = model(**inputs, labels=inputs["input_ids"])
+```

adapter_config.json

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+{
+  "auto_mapping": null,
+  "base_model_name_or_path": "ybelkada/gpt-j-6b-sharded-bf16",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "lora_alpha": 32,
+  "lora_dropout": 0.05,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

adapter_model.bin

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added_tokens.json

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pytorch_model.bin

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special_tokens_map.json

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tokenizer_config.json

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