Update README.md

README.md

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 # **QwQ-LCoT-7B-Instruct Model File**
 
-The **QwQ-LCoT-7B-Instruct** is a fine-tuned language model designed for advanced reasoning and instruction-following tasks. It leverages the **Qwen2.5-7B** base model and has been fine-tuned on the **amphora/QwQ-LongCoT-130K** dataset, focusing on chain-of-thought (CoT) reasoning.
-
-| **File Name**                         | **Size**       | **Description**                                  | **Upload Status**  |
-|----------------------------------------|----------------|-------------------------------------------------|--------------------|
-| `.gitattributes`                       | 1.57 kB        | Tracks large files with Git LFS.                | Uploaded           |
-| `README.md`                            | 273 Bytes      | Contains initial documentation, likely minimal. | Updated            |
-| `added_tokens.json`                    | 657 Bytes      | Maps additional tokens for the tokenizer.       | Uploaded           |
-| `config.json`                          | 848 Bytes      | Model configuration (basic setup).              | Uploaded           |
-| `generation_config.json`               | 281 Bytes      | Settings for text generation tasks.             | Uploaded           |
-| `merges.txt`                           | 1.82 MB        | Tokenizer merges for byte-pair encoding (BPE).  | Uploaded           |
-| `model-00001-of-00004.safetensors`     | 4.88 GB        | First part of model weights (split for LFS).    | Uploaded (LFS)     |
-| `model-00002-of-00004.safetensors`     | 4.93 GB        | Second part of model weights.                   | Uploaded (LFS)     |
-| `model-00003-of-00004.safetensors`     | 4.33 GB        | Third part of model weights.                    | Uploaded (LFS)     |
-| `model-00004-of-00004.safetensors`     | 1.09 GB        | Fourth part of model weights.                   | Uploaded (LFS)     |
-| `model.safetensors.index.json`         | 29.5 kB        | Index file for managing model shards.           | Uploaded           |
-| `special_tokens_map.json`              | 644 Bytes      | Maps special tokens like `<pad>` or `<eos>`.    | Uploaded           |
-| `tokenizer.json`                       | 11.4 MB        | Pre-trained tokenizer file in JSON format.      | Uploaded (LFS)     |
-| `tokenizer_config.json`                | 7.73 kB        | Configuration details for the tokenizer.        | Uploaded           |
-| `vocab.json`                           | 2.78 MB        | Tokenizer vocabulary.                           | Uploaded           |
+The QwQ-LCoT-7B-Instruct is a fine-tuned language model designed for advanced reasoning and instruction-following tasks. It leverages the Qwen2.5-7B base model and has been fine-tuned on the amphora/QwQ-LongCoT-130K dataset, focusing on chain-of-thought (CoT) reasoning. This model is optimized for tasks requiring logical reasoning, detailed explanations, and multi-step problem-solving, making it ideal for applications such as instruction-following, text generation, and complex reasoning tasks.
+
+## Quickstart with Transformers
+
+Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/QwQ-LCoT-7B-Instruct"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "How many r in strawberry."
+messages = [
+    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+```
 
 ### **Sample Long CoT:**