Update README.md

README.md

@@ -6,6 +6,37 @@ license: apache-2.0
 
 #### Notice: The input should contain 4 context examples and the cutoff length should be set to 2048 to ensure best performance.
 
-More usage will be released later
+A simple inference example
+```
+from transformers import AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("phenixace/ICMA-Galactica-125M-M2C")
+
+from transformers import AutoTokenizer
+tk = AutoTokenizer.from_pretrained("phenixace/ICMA-Galactica-125M-M2C")
+
+text ="""Generate a molecule for the caption: The molecule is a fluorinated steroid that is pregn-4-ene substituted by a fluoro group at position 2, a methyl group at position 2 and oxo groups at positions 3, 11 and 20. It is a 3-oxo-Delta(4) steroid, an 11-oxo steroid, a 20-oxo steroid and a fluorinated steroid. It derives from a progesterone. It derives from a hydride of a pregnane.
+Molecule: C[C@@H]1C[C@]2(C(=CC1=O)CC[C@@H]3[C@@]2(C(=O)C[C@]4([C@H]3CC[C@@H]4C(=O)C)C)F)C
+
+Generate a molecule for the caption: The molecule is a steroid ester that is pregn-4-en-21-yl acetate substituted by oxo group at positions 3 and 20, a methyl group at position 6 and hydroxy groups at positions 11 and 17 respectively. It is a 3-oxo-Delta(4) steroid, a steroid ester, an 11beta-hydroxy steroid, a 17alpha-hydroxy steroid, a 20-oxo steroid and a tertiary alpha-hydroxy ketone. It derives from a hydride of a pregnane.
+Molecule: C[C@H]1C[C@H]2[C@@H]3CC[C@@]([C@]3(C[C@@H]([C@@H]2[C@@]4(C1=CC(=O)CC4)C)O)C)(C(=O)COC(=O)C)O
+
+Based on the above examples, analyse the similarities and differences between the examples and finally generate a molecule for the caption: The molecule is a steroid ester that is methyl (17E)-pregna-4,17-dien-21-oate substituted by oxo groups at positions 3 and 11. It is a 3-oxo-Delta(4) steroid, an 11-oxo steroid, a steroid ester and a methyl ester. It derives from a hydride of a pregnane."""
+generation_config = GenerationConfig(
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.85,
+            top_k=40,
+            num_beams=1,
+            repetition_penalty=1.0,
+            pad_token_id=0,
+        )
+inputs = tk(text, return_tensors="pt", return_token_type_ids=False)
+outputs = model.generate(**inputs, return_dict_in_generate=True, output_scores=True, num_return_sequences=1, max_new_tokens=256, generation_config=generation_config)
+
+# decode
+decoded = tk.decode(outputs.sequences[0], skip_special_tokens=True)
+print(decoded)
+```
 
 Paper Link: https://arxiv.org/abs/2403.04197