add example usage

README.md

@@ -1,3 +1,62 @@
 ---
 license: apache-2.0
 ---
+
+
+
+## usage :
+```python
+import os 
+import torch
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+# set HF_TOKEN in terminal as export HF_TOKEN=hf_***
+auth_token = os.environ.get("HF_TOKEN", True)
+
+model_name = "Writer/camel-5b"
+
+tokenizer = AutoTokenizer.from_pretrained(
+   model_name, use_auth_token=auth_token
+)
+model = AutoModelForCausalLM.from_pretrained(
+   model_name,
+    device_map="auto",
+    torch_dtype=torch.float16,
+    use_auth_token=auth_token,
+)
+
+
+instruction = "Describe a futuristic device that revolutionizes space travel."
+
+
+PROMPT_DICT = {
+    "prompt_input": (
+        "Below is an instruction that describes a task, paired with an input that provides further context. "
+        "Write a response that appropriately completes the request\n\n"
+        "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
+    ),
+    "prompt_no_input": (
+        "Below is an instruction that describes a task. "
+        "Write a response that appropriately completes the request.\n\n"
+        "### Instruction:\n{instruction}\n\n### Response:"
+    ),
+}
+
+text = (
+    PROMPT_DICT["prompt_no_input"].format(instruction=instruction)
+    if not input
+    else PROMPT_DICT["prompt_input"].format(instruction=instruction, input=input)
+)
+
+model_inputs = tokenizer(text, return_tensors="pt").to("cuda")
+output_ids = model.generate(
+    **model_inputs,
+    max_length=100,
+)
+output_text = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+clean_output = output_text.split("### Response:")[1].strip()
+
+print(clean_output)
+
+```