Update README.md

README.md

@@ -79,22 +79,34 @@ Rank-16 LoRA adapter fine-tuned from **`deepseek-ai/DeepSeek-Prover-V2-7B`** on
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import peft, torch
 
-adapter_id = "haielab/DeepSeek-Prover-V2-7B-LoRA-v1"
+# --- Hub repo IDs -----------------------------------------------------------
+adapter_id = "haielab/DeepSeek-Prover-V2-7B-conjecture-base-FineTune-new-config"
 base_id    = "deepseek-ai/DeepSeek-Prover-V2-7B"
 
+# --- 1️⃣  Tokenizer ----------------------------------------------------------
 tok = AutoTokenizer.from_pretrained(base_id, trust_remote_code=True)
-tok.padding_side, tok.pad_token = "left", tok.eos_token
+tok.padding_side, tok.pad_token = "left", tok.eos_token     # DeepSeek expects left-padding
 
+# --- 2️⃣  Load base model ----------------------------------------------------
 base = AutoModelForCausalLM.from_pretrained(
     base_id,
     torch_dtype=torch.bfloat16,
     attn_implementation="flash_attention_2",
-    device_map="auto",
+    device_map="auto",          # auto-place on available GPU(s)
 )
+
+# --- 3️⃣  Inject LoRA adapter ------------------------------------------------
 model = peft.PeftModel.from_pretrained(base, adapter_id)
 model.eval()
 
-prompt = "<user>Theorem foo …</user><assistant>"
-inputs = tok(prompt, return_tensors="pt").to(model.device)
-out = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.9)
+# --- 4️⃣  Generate proof continuation ---------------------------------------
+prompt  = "<user>Theorem foo …</user><assistant>"
+inputs  = tok(prompt, return_tensors="pt").to(model.device)
+
+out = model.generate(
+        **inputs,
+        max_new_tokens=256,
+        temperature=0.7,
+        top_p=0.9,
+)
 print(tok.decode(out[0], skip_special_tokens=True))