Update README.md

README.md

@@ -56,7 +56,8 @@ model = AutoModelForCausalLM.from_pretrained(
     token=hf_token,
     cache_dir=hf_cache_dir,
     torch_dtype=torch.bfloat16,
-    attn_implementation="sdpa"
+    attn_implementation="sdpa",
+    mode='s'
 ).to(device)
 model.generation_config.pad_token_id = tokenizer.eos_token_id
 
@@ -65,7 +66,9 @@ prompt = "Describe basics of DNNs quantization."
 inputs = tokenizer(prompt, return_tensors="pt")
 inputs.to(device)
 
-generate_ids = model.generate(**inputs, max_length=500)
+with torch.inference_mode:
+    generate_ids = model.generate(**inputs, max_length=500)
+
 input_len = inputs['input_ids'].shape[1]
 generate_ids = generate_ids[:, input_len:]
 output = tokenizer.batch_decode(
@@ -126,13 +129,10 @@ For quality evaluation we have used: #TODO link to github
 | Winogrande    | 0 | 0 | 0 | 0  | 0        | 0          |
 
 
-> __MMLU__: Evaluates/shows {MMLU} 
-
-> __MMLU__: Evaluates/shows ... 
-
-> __Arc Challenge__: Evaluates/shows ...
-
-> __PIQA__: Evaluates/shows ... 
+* **MMLU**:Evaluates general knowledge across 57 subjects including science, humanities, engineering, and more. Shows model's ability to handle diverse academic topics.
+* **PIQA**: Evaluates physical commonsense reasoning through questions about everyday physical interactions. Shows model's understanding of real-world physics concepts.
+* **Arc Challenge**: Evaluates grade-school level multiple-choice questions requiring reasoning. Shows model's ability to solve complex reasoning tasks.
+* **Winogrande**: Evaluates commonsense reasoning through sentence completion tasks. Shows model's capability to understand context and resolve ambiguity.
 
 ### Latency benchmarks