Update README.md

README.md

@@ -2,6 +2,7 @@
 library_name: peft
 model_name: lora_1B_TR
 tags:
+- meta-llama/Llama-3.2-1B-Instruct
 - lora
 - sft
 - transformers
@@ -9,31 +10,69 @@ tags:
 - unsloth
 licence: license
 pipeline_tag: text-generation
+base_model: meta-llama/Llama-3.2-1B-Instruct
+datasets:
+- kadirnar/combined-turkish-datasets-v5
+language:
+- tr
+- en
 ---
-- base_model:adapter:/home/tk/Desktop/Folder/projects/AI/Models/Llama-3.2-1B-Instruct/
-base_model: /home/tk/Desktop/Folder/projects/AI/Models/Llama-3.2-1B-Instruct/
+
 
 
 # Model Card for lora-out-1B
 
-This model is a fine-tuned version of [None](https://huggingface.co/None).
-It has been trained using [TRL](https://github.com/huggingface/trl).
+This model is a fine-tuned version of 'meta-llama/Llama-3.2-1B-Instruct'.
+
 
 ## Quick start
 
 ```python
-from transformers import pipeline
-
-question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
-generator = pipeline("text-generation", model="None", device="cuda")
-output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
-print(output["generated_text"])
+from unsloth import FastLanguageModel
+from peft import PeftModel
+from transformers import AutoTokenizer
+
+BASE = "meta-llama/Llama-3.2-1B-Instruct"
+ADAPTER = "Codex07/Lora_1B_TR"
+
+# Load Model
+model, tok = FastLanguageModel.from_pretrained(
+    model_name=BASE, max_seq_length=2048, load_in_4bit=False, dtype=None, device_map="auto"
+)
+# Load Adaptor
+model = PeftModel.from_pretrained(model, ADAPTER)  # adapter’ı Unsloth modeline tak
+FastLanguageModel.for_inference(model)
+
+# Test
+messages = [
+    {"role":"system","content":"You are AI assistant. Give user answers"},# Sen bir Yapay Zeka Asistanısısın. kullanıcıdan gelen sorulara resmi cevap ver.
+    {"role":"user","content":"Hi, can you help me?"},
+    {"role":"assistant","content":"I would like to help you."},
+    {"role":"user","content":"How many tools do you have?"},
+]
+prompt = tok.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(prompt, max_new_tokens=2048)
+print(tok.decode(out[0, prompt.shape[-1]:], skip_special_tokens=True))
 ```
 
 ## Training procedure
+Half of 'kadirnar/combined-turkish-datasets-v5' Turkish dataset used. 
+Dataset divided into chunks by size 65k.
 
- 
-
+```bibtex
+1> 2:50:33 / 2.746500 -> 1.771400 / 5.1.0
+2> 3:00:00 / 1.7 -> 1.7           / 5.1.1
+3> 2:18:19 / 1.859100 -> 1.474300 / 5.1.2
+4> 3:15:13 / 1.421800 -> 1.122000 / 5.1.3
+5> 2:50:00 / 1.746600 -> 1.629600 / 5.1.0
+6> 2:44:46 / 1.745000 -> 1.653300 / 5.1.1
+7> 2:07:00 / 1.478200 -> 1.357400 / 5.1.2
+8> 3:11:54 / 1.174700 -> 1.046100 / 5.1.3
+9> 3:12:39 / 1.117600 -> 0.796700 / 5.2.0
+10>1:00:57 / 2.217400 -> 1.741400 / 5.2.1
+11>1:30:04 / 2.919900 -> 2.534300 / 5.2.2
+12>1:30:05 / 2.534300 -> 2.320100 / 5.2.2 
+```
 
 This model was trained with SFT.
 
@@ -48,10 +87,7 @@ This model was trained with SFT.
 
 ## Citations
 
-
-
 Cite TRL as:
-    
 ```bibtex
 @misc{vonwerra2022trl,
 	title        = {{TRL: Transformer Reinforcement Learning}},
@@ -61,4 +97,4 @@ Cite TRL as:
 	publisher    = {GitHub},
 	howpublished = {\url{https://github.com/huggingface/trl}}
 }
-```
+```