Push model using huggingface_hub.

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,43 @@
+---
+license: apache-2.0
+tags:
+- trl
+- ppo
+- 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="DarshanDeshpande//tmp/SLURM_20252742/tmp8za0h0al/DarshanDeshpande/gemma_2b_oasst1_ppo_model")
+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("DarshanDeshpande//tmp/SLURM_20252742/tmp8za0h0al/DarshanDeshpande/gemma_2b_oasst1_ppo_model")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("DarshanDeshpande//tmp/SLURM_20252742/tmp8za0h0al/DarshanDeshpande/gemma_2b_oasst1_ppo_model")
+
+inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
+outputs = model(**inputs, labels=inputs["input_ids"])
+```

adapter_config.json

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+  "alpha_pattern": {},
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+  "base_model_name_or_path": "google/gemma-2b",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 8,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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config.json

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+{
+  "exp_name": "train_ppo",
+  "seed": 0,
+  "log_with": "wandb",
+  "task_name": null,
+  "model_name": "google/gemma-2b",
+  "query_dataset": "imdb",
+  "reward_model": "sentiment-analysis:lvwerra/distilbert-imdb",
+  "remove_unused_columns": true,
+  "tracker_kwargs": {},
+  "accelerator_kwargs": {},
+  "project_kwargs": {},
+  "tracker_project_name": "trl",
+  "push_to_hub_if_best_kwargs": {
+    "repo_id": "gemma_2b_oasst1_ppo_model"
+  },
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+  "is_peft_model": true,
+  "backward_batch_size": 16,
+  "global_backward_batch_size": 16,
+  "global_batch_size": 16
+}

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special_tokens_map.json

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tokenizer_config.json

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+  "tokenizer_class": "GemmaTokenizer",
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+  "use_default_system_prompt": false
+}