Update README.md (Remove Duplicates)

README.md

@@ -1,22 +1,3 @@
----
-base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
-library_name: peft
-pipeline_tag: text-generation
-tags:
-- base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
-- lora
-- transformers
-license: apache-2.0
-datasets:
-- manu/project_gutenberg
-- oscar-corpus/oscar
-- sedthh/gutenberg_english
-language:
-- en
----
-
-# Model Card for Model ID
-
 ---
 language:
 - en
@@ -39,7 +20,9 @@ model-index:
   results: []
 ---
 
-# SamKash-Tolstoy — DeepSeek LoRA (Russian Literature)
+# Model Card for Model ID
+
+# SamKash-Tolstoy - DeepSeek LoRA (Russian Literature)
 
 **Developed by Kashif Salahuddin and Samiya Kashif**, **SamKash-Tolstoy** is a domain-specialized LLM (lightweight LoRA adapter) built exclusively for Russian literature. It’s trained on **475 public-domain Russian classics** from the Project Gutenberg collection and enriched with **university and critics’ articles** filtered from the **OSCAR** web corpus, so the voice and psychological depth feel authentic without using any copyrighted books.
 
@@ -55,8 +38,6 @@ model-index:
 
 **Example prompt:** “Write a short scene in the style of Crime and Punishment: a feverish student crosses a Petersburg bridge at night.”
 
-
-
 ---
 
 ## TL;DR: Use It
@@ -88,9 +69,6 @@ out = gen(
 )[0]["generated_text"]
 print(out)
 
-
-
-
 ## Model Details
 
 ### Model Description
@@ -151,32 +129,4 @@ print(out)
 ### Recommendations
 - Keep a **human in the loop** for editing and intent verification.
 - Avoid representing outputs as genuine text by historical authors.
-- For classroom settings, clearly label generated content as synthetic.
-
----
-
-## How to Get Started with the Model
-
-```python
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from peft import PeftModel
-
-base_id = "deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"
-adpt_id = "salakash/SamKash-Tolstoy"  # or local folder
-
-device = "mps" if torch.backends.mps.is_available() else "cpu"
-dtype  = torch.float16 if device == "mps" else torch.float32
-
-tok = AutoTokenizer.from_pretrained(base_id, use_fast=True)
-base = AutoModelForCausalLM.from_pretrained(base_id, dtype=dtype)
-base.to(device)
-
-model = PeftModel.from_pretrained(base, adpt_id)
-model.config.use_cache = True  # inference
-
-gen = pipeline("text-generation", model=model, tokenizer=tok, device=-1)
-print(gen(
-    "Write a reflective paragraph about conscience and fate in an aristocratic household.",
-    max_new_tokens=200, do_sample=True, temperature=0.7, top_p=0.9
-)[0]["generated_text"])
+- For classroom settings, clearly label generated content as synthetic.