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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+phi-pl-2_7B-v_0_1 - bnb 4bits
+- Model creator: https://huggingface.co/teddy-f-47/
+- Original model: https://huggingface.co/teddy-f-47/phi-pl-2_7B-v_0_1/
+
+
+
+
+Original model description:
+---
+license: mit
+base_model: microsoft/phi-2
+tags:
+- generated_from_trainer
+model-index:
+- name: phi-2-pl-v_0_1
+  results: []
+---
+
+# phi-2-pl-v_0_1
+
+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
+
+The model was trained for a context length of 2048 tokens.
+
+## Intended uses & limitations
+
+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
+
+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
+- 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
+
+- Transformers 4.37.1
+- Pytorch 2.1.2
+- Datasets 2.16.1
+- Tokenizers 0.15.1
+
+