Update README.md

README.md

@@ -23,3 +23,61 @@ The training process was managed using the robust framework provided by MosaicML
 - attn_impl: flash
 - Trained on 8 H100 GPU on GCP
 
+## Datasets
+
+
+
+## How to Use
+The basic use cases to generate text using this model are simple. Follow the below code to generate text using this model.
+
+Install the following library before running the code:
+
+```sh
+pip install transformers
+pip install einops
+pip install accelerate
+```
+
+```py
+import transformers
+from transformers import pipeline
+
+model_name = 'hishab/titulm-1b-enbn-v1'
+
+config = transformers.AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+config.max_seq_len = 2048
+
+model = transformers.AutoModelForCausalLM.from_pretrained(
+  model_name,
+  config=config,
+  trust_remote_code=True
+)
+
+tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
+
+pipe = pipeline('text-generation', model=model, tokenizer=tokenizer, device='cuda:0')
+# for Bangla
+bn_output = pipe('আমি বাংলায় গান',
+            max_new_tokens=100,
+            do_sample=True,
+            use_cache=True)
+
+print(bn_output)
+# for English
+en_output = pipe('Bangla language plays',
+            max_new_tokens=100,
+            do_sample=True,
+            use_cache=True)
+
+print(en_output)
+```
+
+## Citation
+```bash
+@misc{hishab_2024_titulm_1b_enbn_v1,
+  author = {Hishab Technologies Ltd.},
+  title = {TituLM-1B-ENBN-V1},
+  year = {2024},
+  publisher = {HuggingFace Models},
+  howpublished = {https://huggingface.co/hishab/titulm-1b-enbn-v1},
+}