Update README.md

README.md

@@ -11,6 +11,63 @@ language:
 - en
 ---
 
+# Usage 
+
+4-bit-quantized Qwen 0.6B fine-tuned on the english version of `brighter-dataset/BRIGHTER-emotion-categories'.
+
+To use the model:
+
+```
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "FritzStack/QWEmotioN-4bit", 
+    trust_remote_code=True
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "FritzStack/QWEmotioN-4bit",
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True
+)
+
+def predict_emotions(text, max_new_tokens=50):
+    """
+    Predict emotions for a given text
+    """
+    prompt = f"{text}. "
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=False,
+            #temperature=0.8,
+            top_k = 10,
+            #repetition_penalty=35.,
+            pad_token_id=tokenizer.eos_token_id
+        )
+
+    generated_text = tokenizer.decode(
+        outputs[0][len(inputs.input_ids[0]):],
+        skip_special_tokens=False
+    ).strip()
+
+    return generated_text
+```
+
+
+```
+print(predict_emotions("I miss you"))
+### Output
+Emotion Output: sadness <|im_end|>
+```
+
+
 # Uploaded  model
 
 - **Developed by:** FritzStack