update model card README.md

README.md

@@ -1,20 +1,57 @@
 ---
-library_name: peft
+base_model: meta-llama/Llama-2-7b-chat-hf
+tags:
+- generated_from_trainer
+model-index:
+- name: llama-2-7b-reward-oasst1
+  results: []
 ---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# llama-2-7b-reward-oasst1
+
+This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset.
+It achieves the following results on the evaluation set:
+- eval_loss: 0.5062
+- eval_accuracy: 0.7593
+- eval_runtime: 431.9996
+- eval_samples_per_second: 2.058
+- eval_steps_per_second: 2.058
+- epoch: 0.72
+- step: 3000
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
 ## Training procedure
 
+### Training hyperparameters
 
-The following `bitsandbytes` quantization config was used during training:
-- load_in_8bit: False
-- load_in_4bit: True
-- llm_int8_threshold: 6.0
-- llm_int8_skip_modules: None
-- llm_int8_enable_fp32_cpu_offload: False
-- llm_int8_has_fp16_weight: False
-- bnb_4bit_quant_type: nf4
-- bnb_4bit_use_double_quant: False
-- bnb_4bit_compute_dtype: float16
-### Framework versions
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 1
 
+### Framework versions
 
-- PEFT 0.5.0.dev0
+- Transformers 4.32.0.dev0
+- Pytorch 2.0.1+cu118
+- Datasets 2.14.0
+- Tokenizers 0.13.3