Update README.md

README.md

@@ -8,42 +8,68 @@ model-index:
   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. -->
-
 # phi-2-pl-v_0_1
 
-This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
+This model is based on [microsoft/phi-2](https://huggingface.co/microsoft/phi-2). It was trained from scratch on the 20231201 Polish Wikipedia dump.
 
 ## Model description
 
-More information needed
+The model was trained for a context length of 2048 tokens.
 
 ## Intended uses & limitations
 
-More information needed
+The model is intended for research purposes only. It may generate fictitious, incorrect, unethical, or biased texts. At its current state, it is not suitable for production purposes.
+
+Example:
+```
+tokenizer = AutoTokenizer.from_pretrained(
+    model_name, trust_remote_code=True, use_fast=True
+)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name, vocab_size=len(tokenizer), attn_implementation="flash_attention_2",
+    trust_remote_code=True, torch_dtype=torch.bfloat16, device_map="auto"
+)
+model.eval()
+
+generation_config = GenerationConfig.from_pretrained(
+    model_name, do_sample=False, repetition_penalty=1.5,
+    min_new_tokens=1, max_new_tokens=128
+)
+
+test_input = tokenizer("Wrocław to polski miasto. Wrocław jest ", return_tensors='pt').to(torch.device('cuda'))
+test_output = model.generate(**test_input, generation_config=generation_config)
+test_preds = tokenizer.batch_decode(sequences=test_output, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+print(test_preds)
+```
 
 ## Training and evaluation data
 
-More information needed
+The 20231201 Polish Wikipedia dump.
 
 ## Training procedure
 
+### Training environment
+
+- GPU: 1 x A100X (80GB)
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
 - learning_rate: 0.0002
+- num_devices: 1
 - train_batch_size: 8
-- eval_batch_size: 8
-- seed: 42
+- gradient_accumulation_steps: 1
 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07
 - lr_scheduler_type: cosine
 - lr_scheduler_warmup_ratio: 0.1
 - num_epochs: 1
+- precision: bf16
+- seed: 42
 
 ### Training results
 
-
+- runtime: 1mo 3d 9h 40m 16s
+- train_loss: 2.983
 
 ### Framework versions