Push model using huggingface_hub.

README.md

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+---
+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="yuchiz//tmp/tmpbswj31mg/yuchiz/models")
+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("yuchiz//tmp/tmpbswj31mg/yuchiz/models")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("yuchiz//tmp/tmpbswj31mg/yuchiz/models")
+
+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": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "meta-llama/Llama-2-7b-chat-hf",
+  "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": 32,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "layers.27.self_attn.q_proj",
+    "layers.31.self_attn.q_proj",
+    "layers.27.self_attn.v_proj",
+    "layers.25.self_attn.v_proj",
+    "layers.24.self_attn.q_proj",
+    "layers.26.self_attn.q_proj",
+    "layers.25.self_attn.q_proj",
+    "layers.28.self_attn.q_proj",
+    "layers.29.self_attn.v_proj",
+    "layers.30.self_attn.q_proj",
+    "layers.24.self_attn.v_proj",
+    "layers.30.self_attn.v_proj",
+    "layers.26.self_attn.v_proj",
+    "layers.29.self_attn.q_proj",
+    "layers.31.self_attn.v_proj",
+    "layers.28.self_attn.v_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

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+oid sha256:73b03b0030ff0ca5d523e95c87c70ea936c2bd171420dedb66a688a078ab902b
+size 8392896

config.json

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+{
+  "exp_name": "hotpot_rl",
+  "seed": 0,
+  "log_with": null,
+  "task_name": null,
+  "model_name": "gpt2",
+  "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": {},
+  "steps": 20000,
+  "learning_rate": 1e-05,
+  "adap_kl_ctrl": true,
+  "init_kl_coef": 0.2,
+  "kl_penalty": "kl",
+  "target": 6,
+  "horizon": 10000,
+  "gamma": 1,
+  "lam": 0.95,
+  "cliprange": 0.2,
+  "cliprange_value": 0.2,
+  "vf_coef": 0.1,
+  "batch_size": 32,
+  "forward_batch_size": null,
+  "mini_batch_size": 1,
+  "gradient_accumulation_steps": 8,
+  "world_size": 4,
+  "ppo_epochs": 4,
+  "max_grad_norm": null,
+  "optimize_cuda_cache": true,
+  "optimize_device_cache": false,
+  "early_stopping": false,
+  "target_kl": 1,
+  "compare_steps": 1,
+  "ratio_threshold": 10.0,
+  "use_score_scaling": false,
+  "use_score_norm": false,
+  "score_clip": null,
+  "whiten_rewards": false,
+  "is_encoder_decoder": false,
+  "is_peft_model": true,
+  "backward_batch_size": 8,
+  "global_backward_batch_size": 32,
+  "global_batch_size": 128
+}

pytorch_model.bin

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+size 17916