update feedback after the running in the omnicamp.us

README.md

@@ -53,7 +53,7 @@ conda install -c conda-forge ipykernel
 python -m ipykernel install --user --name=unsloth_env --display-name "Python (unsloth_env)"  
 ```
 
-## Follow these steps in the notebook:
+## Follow these steps, run in the notebook:
 
 1. load model
 ```shell
@@ -78,14 +78,15 @@ model, tokenizer = FastLanguageModel.from_pretrained(
     max_seq_length = max_seq_length,
     dtype = dtype,
     load_in_4bit = load_in_4bit,
-    # token = "HF token", # In normal cases, it call from ENV. If you want to write the token directly, please comment it out.
+    # token = "hf-token", # In the Google Colab case, it call from ENV. If you want to write the token directly, please comment it out.
 )
 FastLanguageModel.for_inference(model)
-
 ```
 
 3. Set up datasets and run inference.
 
+- Upload elyza-tasks-100-TV_0.jsonl to your workspace in manual.
+
 ```python
 datasets = []
 with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
@@ -96,7 +97,9 @@ with open("./elyza-tasks-100-TV_0.jsonl", "r") as f:
       if item.endswith("}"):
         datasets.append(json.loads(item))
         item = ""
+```
 
+```python
 from tqdm import tqdm
 
 # inference
@@ -118,9 +121,8 @@ for dt in tqdm(datasets):
 
 ```python
 file_name = model_name.replace("/", "_") + "_output.jsonl"
-with open(f"/content/{file_name}", 'w', encoding='utf-8') as f:
+with open(f"./{file_name}", 'w', encoding='utf-8') as f:
     for result in results:
         json.dump(result, f, ensure_ascii=False)
         f.write('\n')
-
 ```