Update README.md

README.md

@@ -1,40 +1,46 @@
----
-    license: apache-2.0
-    tags:
-      - tinyllama
-      - causal-lm
-      - merged-lora
-    base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
-    merged_from:
-      - snaplora-adapted
-    ---
-
-    # TinyLlama (Merged LoRA)
-
-    This repository contains a TinyLlama model with LoRA weights merged into the base.
-
-    - **Base model:** `TinyLlama/TinyLlama-1.1B-Chat-v1.0`  
-    - **Adapter:** `snaplora-adapted`  
-    - **Merge date:** 2025-09-14 23:12:26Z UTC
-
-    ## Usage
-
-    ```python
-    from transformers import AutoModelForCausalLM, AutoTokenizer
-    import torch
-
-    model_id = "<this-repo-id>"
-    tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
-    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
-
-    prompt = "Write a haiku about tiny models."
-    inputs = tok(prompt, return_tensors="pt").to(model.device)
-    with torch.no_grad():
-        out = model.generate(**inputs, max_new_tokens=64)
-    print(tok.decode(out[0], skip_special_tokens=True))
-    ```
-
-    ## Notes
-
-    - The adapter was merged into the base weights using `peft.PeftModel.merge_and_unload()`.
-    - Files are saved with `safetensors` when possible.
+---
+datasets:
+- Tesslate/UIGEN-T2
+base_model:
+- TinyLlama/TinyLlama-1.1B-Chat-v1.0
+---
+---
+    license: apache-2.0
+    tags:
+      - tinyllama
+      - causal-lm
+      - merged-lora
+    base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+    merged_from:
+      - snaplora-adapted
+    ---
+
+    # TinyLlama (Merged LoRA)
+
+    This repository contains a TinyLlama model with LoRA weights merged into the base.
+
+    - **Base model:** `TinyLlama/TinyLlama-1.1B-Chat-v1.0`  
+    - **Adapter:** `snaplora-adapted`  
+    - **Merge date:** 2025-09-14 23:12:26Z UTC
+
+    ## Usage
+
+    ```python
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    import torch
+
+    model_id = "<this-repo-id>"
+    tok = AutoTokenizer.from_pretrained(model_id, use_fast=True)
+    model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
+
+    prompt = "Write a haiku about tiny models."
+    inputs = tok(prompt, return_tensors="pt").to(model.device)
+    with torch.no_grad():
+        out = model.generate(**inputs, max_new_tokens=64)
+    print(tok.decode(out[0], skip_special_tokens=True))
+    ```
+
+    ## Notes
+
+    - The adapter was merged into the base weights using `peft.PeftModel.merge_and_unload()`.
+    - Files are saved with `safetensors` when possible.