Add model card with exact and within-1 confusion matrices and per-class metrics

README.md

@@ -24,7 +24,7 @@ This is a fine-tuned version of `unsloth/gemma-7b-bnb-4bit` for CEFR-level sente
 - **Other Metrics**:
   - Perplexity: 5.344
   - Diversity (Unique Sentences): 0.100
-  - Inference Time (ms): 5869.541
+  - Inference Time (ms): 5802.883
   - Model Size (GB): 4.8
   - Robustness (F1): 0.045
 - **Confusion Matrix (Exact Matches)**:
@@ -51,8 +51,8 @@ This is a fine-tuned version of `unsloth/gemma-7b-bnb-4bit` for CEFR-level sente
   ```python
   from transformers import AutoModelForCausalLM, AutoTokenizer
 
-  model = AutoModelForCausalLM.from_pretrained("Mr-FineTuner/Test_02_llama_trainPercen_myValidator")
-  tokenizer = AutoTokenizer.from_pretrained("Mr-FineTuner/Test_02_llama_trainPercen_myValidator")
+  model = AutoModelForCausalLM.from_pretrained("Mr-FineTuner/Test_02_llama_trainPercen_myValidator_2ndTry")
+  tokenizer = AutoTokenizer.from_pretrained("Mr-FineTuner/Test_02_llama_trainPercen_myValidator_2ndTry")
 
   # Example inference
   prompt = "<|user|>Generate a CEFR B1 level sentence.<|end|>"