Create README.md

README.md

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+# Model Card: Streaming Terminal Log Boundary Predictor (Phi-4 LoRA)
+
+## 🤖 Model Details
+* **Base Model:** `unsloth/Phi-4-unsloth-bnb-4bit` (14B Parameters)
+* **Architecture:** LoRA Adapters (PEFT)
+* **Task:** Binary Classification (Terminal Event Boundary Detection)
+* **Quantization:** 4-bit (bitsandbytes)
+* **Language:** English / Bash / Terminal XML
+
+## 🎯 Intended Use
+This model is designed to parse continuous, timestamped terminal logs formatted in XML and determine if a specific line represents a "Boundary." 
+* **New Event:** The start of a new phase (e.g., a new user prompt appearing, or a transition from downloading to extracting).
+* **Old Event:** A continuation of an ongoing process or a user keystroke (e.g., pressing Enter).
+
+This is particularly useful for segmenting long, automated build logs (like `apt-get` installations or mirror syncs) into readable, distinct chronological events.
+
+## 🗂️ Training Data
+The model was fine-tuned on the **[Jaiccc/model0_boundary_predict_streaming](https://huggingface.co/datasets/Jaiccc/model0_boundary_predict_streaming)** dataset. 
+
+The data utilizes a **sliding-window context of 15 chunks**:
+* 14 chunks of historical context.
+* 1 Target chunk to classify.
+* Heavily imbalanced data was downsampled to a 2:1 (Old:New) ratio to prevent majority-class guessing.
+
+## 💻 How to Use (Inference)
+Because this is a private repository, you must authenticate using your Hugging Face token. This model uses PEFT/LoRA, meaning it is highly memory-efficient.
+
+```python
+import re
+from unsloth import FastLanguageModel
+from google.colab import userdata
+
+# 1. Retrieve your secure token
+hf_token = userdata.get('HF_TOKEN') # Or os.getenv("HF_TOKEN")
+
+# 2. Load the Fine-Tuned Model
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name = "Jaiccc/model_0_streaming_timestamp",
+    max_seq_length = 4096,
+    load_in_4bit = True,
+    token = hf_token, 
+)
+FastLanguageModel.for_inference(model)
+
+# 3. Format your prompt (ChatML)
+instruction = "Your task is to analyze terminal XML logs and determine whether the timestamp in the TARGET LINE belongs to a 'new event' or an 'old event'."
+input_data = "### CONTEXT (Previous Events):\n<system_output timestamp=\"10.01\">demo@server:~$ apt update</system_output>\n\n### TARGET LINE:\n<user_input timestamp=\"12.40\">s</user_input>"
+
+prompt = f"<|im_start|>user<|im_sep|>{instruction}\n\n{input_data}<|im_end|><|im_start|>assistant<|im_sep|>"
+inputs = tokenizer(prompt, return_tensors="pt").input_ids.to("cuda")
+
+# 4. Generate Prediction
+outputs = model.generate(input_ids=inputs, max_new_tokens=64, use_cache=True, temperature=0.1)
+raw_output = tokenizer.batch_decode(outputs, clean_up_tokenization_spaces=True)[0]
+
+# 5. Extract Result
+m = re.search(r'<\|im_start\|>assistant<\|im_sep\|>(.*?)<\|im_end\|>', raw_output, re.S)
+result = m.group(1).strip() if m else raw_output.split("assistant")[-1].strip()
+
+print(result)
+# Expected Output: "12.40, old event"