Update README.md

README.md

@@ -54,7 +54,103 @@ python inference.py \
   --score_threshold -1 \
   --seed 42 \
   --device cuda:0
-````
+```
+
+---
+
+## Usage Example
+
+This example shows a typical workflow for a **single user**:
+1) encode text pairs with Skywork-Reward-V2-Llama-3.1-8B into embeddings,
+2) adapt the MRM on the user's few-shot examples (update `shared_weight` only),
+3) run inference on new pairs for that same user.
+
+```python
+import torch
+from copy import deepcopy
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+from utils import bt_loss
+from train import MRM
+from inference import load_ckpt_into_model
+
+
+@torch.no_grad()
+def encode_pairs(model, tokenizer, pairs, device="cuda"):
+    model.eval()
+    ch, rj = [], []
+    for ex in pairs:
+        conv = ex["prompt"]
+        for key, buf in [("chosen", ch), ("rejected", rj)]:
+            ids = tokenizer.apply_chat_template(
+                conv + [{"role": "assistant", "content": ex[key]}],
+                tokenize=True, return_tensors="pt"
+            ).to(device)
+            out = model(ids, output_hidden_states=True)
+            buf.append(out.hidden_states[-1][0, -1].float().cpu())
+    return torch.stack(ch), torch.stack(rj)
+
+
+def adapt_single_user(base_model, support_ch, support_rj, inner_lr=1e-3, inner_epochs=5, device="cuda"):
+    model = deepcopy(base_model).to(device).train()
+    opt = torch.optim.Adam([model.shared_weight], lr=inner_lr)
+    support_ch, support_rj = support_ch.to(device), support_rj.to(device)
+    for _ in range(inner_epochs):
+        opt.zero_grad()
+        loss = bt_loss(model(support_ch), model(support_rj))
+        loss.backward()
+        opt.step()
+    return model.eval()
+
+
+@torch.no_grad()
+def infer_on_pairs(model, ch, rj, device="cuda"):
+    return model(ch.to(device)), model(rj.to(device))
+
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+MODEL_PATH = "Skywork/Skywork-Reward-V2-Llama-3.1-8B"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+llm = AutoModelForSequenceClassification.from_pretrained(
+    MODEL_PATH, num_labels=1, torch_dtype=torch.bfloat16, device_map=device
+)
+
+CKPT_PATH = "ckpt/model.pt"
+mrm = MRM(in_dim=4096, hidden_sizes=[2], use_bias=False)
+load_ckpt_into_model(mrm, CKPT_PATH, device)
+
+support_pairs = [
+    {
+        "prompt": [{"role": "user", "content": "TL;DR this post: I tried waking up at 5am for a month and tracked my productivity."}],
+        "chosen": "Waking up early helped at first, but long-term productivity depended more on sleep quality than wake-up time.",
+        "rejected": "The post is about waking up early and productivity.",
+    },
+    {
+        "prompt": [{"role": "user", "content": "Summarize the main point: I switched from iPhone to Android after 10 years."}],
+        "chosen": "The author values customization and battery life more than ecosystem lock-in, which motivated the switch.",
+        "rejected": "The author bought a new phone.",
+    },
+]
+
+sup_ch, sup_rj = encode_pairs(llm, tokenizer, support_pairs, device)
+user_mrm = adapt_single_user(mrm, sup_ch, sup_rj, device=device)
+
+test_pairs = [
+    {
+        "prompt": [{"role": "user", "content": "TL;DR: I quit my job to freelance and here is what I learned in 6 months."}],
+        "chosen": "Freelancing offers flexibility but requires strong self-discipline and financial planning to be sustainable.",
+        "rejected": "The author talks about quitting a job and freelancing.",
+    }
+]
+
+test_ch, test_rj = encode_pairs(llm, tokenizer, test_pairs, device)
+s_ch, s_rj = infer_on_pairs(user_mrm, test_ch, test_rj, device)
+
+print("reward(chosen)  =", s_ch.tolist())
+print("reward(rejected)=", s_rj.tolist())
+
+```
 
 ---