Add standalone inference notebook cell

INFERENCE_CELL.md

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+# Standalone Inference Cell
+
+Paste this as the **last cell** in Kaggle/Colab after training, or run it in a fresh notebook. It is independent of previous training cells.
+
+## Configure paths
+
+Set:
+
+```python
+BASE_MODEL_ID = "LiquidAI/LFM2.5-1.2B-Instruct"
+ADAPTER_PATH = "./lfm25-stable-qlora-cybersecurity-adapter"
+```
+
+For Unsloth-trained adapters, examples:
+
+```python
+BASE_MODEL_ID = "unsloth/LFM2.5-1.2B-Instruct"
+ADAPTER_PATH = "./lfm25-lora-adapter"
+```
+
+```python
+BASE_MODEL_ID = "unsloth/Qwen3-4B-Instruct-2507-unsloth-bnb-4bit"
+ADAPTER_PATH = "./qwen3-lora-adapter"
+```
+
+If you pushed adapter to Hub:
+
+```python
+ADAPTER_PATH = "your-username/your-adapter-repo"
+```
+
+---
+
+## Complete independent cell
+
+```python
+# ============================================================
+# Standalone LoRA Adapter Inference / Chat Cell
+# Works in fresh Kaggle/Colab notebook too.
+# ============================================================
+!pip install -q -U "transformers>=4.56.0" "peft>=0.18.0" "accelerate>=1.0.0" "bitsandbytes>=0.45.0" "huggingface_hub>=0.25.0"
+
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
+
+# -------------------- CONFIG --------------------
+# Use the SAME base model used during training.
+BASE_MODEL_ID = "LiquidAI/LFM2.5-1.2B-Instruct"
+
+# Local adapter folder OR HF repo id.
+# Common local paths:
+#   ./lfm25-lora-adapter
+#   ./qwen3-lora-adapter
+#   ./gemma4-lora-adapter
+#   ./lfm25-stable-qlora-cybersecurity-adapter
+ADAPTER_PATH = "./lfm25-stable-qlora-cybersecurity-adapter"
+
+LOAD_IN_4BIT = True
+MAX_NEW_TOKENS = 512
+TEMPERATURE = 0.7
+TOP_P = 0.9
+SYSTEM_PROMPT = "You are a helpful assistant. For cybersecurity topics, provide ethical, defensive, authorized guidance only."
+# ------------------------------------------------
+
+compute_dtype = torch.float16
+
+bnb_config = None
+if LOAD_IN_4BIT:
+    bnb_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_use_double_quant=True,
+        bnb_4bit_compute_dtype=compute_dtype,
+    )
+
+print("Loading tokenizer...")
+tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID, trust_remote_code=True, use_fast=True)
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+tokenizer.padding_side = "right"
+
+print("Loading base model...")
+base_model = AutoModelForCausalLM.from_pretrained(
+    BASE_MODEL_ID,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True,
+    low_cpu_mem_usage=True,
+)
+
+print("Loading LoRA adapter...")
+model = PeftModel.from_pretrained(base_model, ADAPTER_PATH, is_trainable=False)
+model.eval()
+
+if torch.cuda.is_available():
+    print(f"VRAM loaded: {torch.cuda.memory_allocated()/1e9:.2f} GB / {torch.cuda.get_device_properties(0).total_memory/1e9:.2f} GB")
+print("✅ Model + adapter ready")
+
+
+def build_prompt(messages):
+    try:
+        return tokenizer.apply_chat_template(
+            messages,
+            tokenize=False,
+            add_generation_prompt=True,
+        )
+    except Exception:
+        text = ""
+        for m in messages:
+            text += f"<{m['role']}>\n{m['content']}\n</{m['role']}>\n"
+        text += "<assistant>\n"
+        return text
+
+
+def ask(prompt, history=None):
+    if history is None:
+        history = [{"role": "system", "content": SYSTEM_PROMPT}]
+    messages = history + [{"role": "user", "content": prompt}]
+    full_prompt = build_prompt(messages)
+    inputs = tokenizer(full_prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        output = model.generate(
+            **inputs,
+            max_new_tokens=MAX_NEW_TOKENS,
+            do_sample=True,
+            temperature=TEMPERATURE,
+            top_p=TOP_P,
+            pad_token_id=tokenizer.pad_token_id,
+            eos_token_id=tokenizer.eos_token_id,
+        )
+    new_tokens = output[0][inputs["input_ids"].shape[1]:]
+    reply = tokenizer.decode(new_tokens, skip_special_tokens=True).strip()
+    return reply
+
+# Single test prompt
+reply = ask("Explain how parameterized queries prevent SQL injection with a safe Python example.")
+print("Assistant:\n", reply)
+```
+
+---
+
+## Optional interactive chat loop
+
+Run this after the cell above:
+
+```python
+history = [{"role": "system", "content": SYSTEM_PROMPT}]
+
+while True:
+    user = input("You: ").strip()
+    if user.lower() in {"exit", "quit", "q"}:
+        break
+    if not user:
+        continue
+
+    reply = ask(user, history)
+    print("Assistant:", reply, "\n")
+    history.append({"role": "user", "content": user})
+    history.append({"role": "assistant", "content": reply})
+```