Create README.md

README.md

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+---
+language:
+- ms
+---
+
+# Pretrain BB 4096 context length Mistral on Malaysian text
+
+README at https://github.com/mesolitica/malaya/tree/5.1/pretrained-model/mistral
+
+- Dataset gathered at https://github.com/malaysia-ai/dedup-text-dataset/tree/main/pretrain-llm
+- We use Ray cluster to train on 5 nodes of 8x A100 80GB, https://github.com/malaysia-ai/jupyter-gpu/tree/main/ray
+
+WandB, https://wandb.ai/mesolitica/pretrain-mistral-5b?workspace=user-husein-mesolitica
+
+WandB report, https://wandb.ai/mesolitica/pretrain-mistral-3b/reports/Pretrain-Larger-Malaysian-Mistral--Vmlldzo2MDkyOTgz
+
+## how-to
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+import torch
+
+TORCH_DTYPE = 'bfloat16'
+nf4_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type='nf4',
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=getattr(torch, TORCH_DTYPE)
+)
+
+tokenizer = AutoTokenizer.from_pretrained('mesolitica/malaysian-mistral-5B-4096')
+model = AutoModelForCausalLM.from_pretrained(
+    'mesolitica/malaysian-mistral-5B-4096',
+    use_flash_attention_2 = True,
+    quantization_config = nf4_config
+)
+prompt = '<s>nama saya'
+inputs = tokenizer([prompt], return_tensors='pt', add_special_tokens=False).to('cuda')
+
+generate_kwargs = dict(
+    inputs,
+    max_new_tokens=512,
+    top_p=0.95,
+    top_k=50,
+    temperature=0.9,
+    do_sample=True,
+    num_beams=1,
+    repetition_penalty=1.05,
+)
+r = model.generate(**generate_kwargs)
+```