Update README.md

README.md

@@ -209,70 +209,6 @@ print(f"{prepare_inputs['sft_format'][0]}", answer)
 python cli_chat.py --model_path deepseek-ai/deepseek-vl-7b-chat
 ```
 
-Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.
-
-```
-User: {messages[0]['content']}
-
-Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}
-
-Assistant:
-```
-
-**Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
-
-### Inference with vLLM
-
-You can also employ [vLLM](https://github.com/vllm-project/vllm) for high-throughput inference.
-
-**Text Completion**
-
-```python
-from vllm import LLM, SamplingParams
-
-tp_size = 4 # Tensor Parallelism
-sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=100)
-model_name = "deepseek-ai/deepseek-llm-67b-base"
-llm = LLM(model=model_name, trust_remote_code=True, gpu_memory_utilization=0.9, tensor_parallel_size=tp_size)
-
-prompts = [
-    "If everyone in a country loves one another,",
-    "The research should also focus on the technologies",
-    "To determine if the label is correct, we need to"
-]
-outputs = llm.generate(prompts, sampling_params)
-
-generated_text = [output.outputs[0].text for output in outputs]
-print(generated_text)
-```
-
-**Chat Completion**
-
-```python
-from transformers import AutoTokenizer
-from vllm import LLM, SamplingParams
-
-tp_size = 4 # Tensor Parallelism
-sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=100)
-model_name = "deepseek-ai/deepseek-llm-67b-chat"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-llm = LLM(model=model_name, trust_remote_code=True, gpu_memory_utilization=0.9, tensor_parallel_size=tp_size)
-
-messages_list = [
-    [{"role": "user", "content": "Who are you?"}],
-    [{"role": "user", "content": "What can you do?"}],
-    [{"role": "user", "content": "Explain Transformer briefly."}],
-]
-# Avoid adding bos_token repeatedly
-prompt_token_ids = [tokenizer.apply_chat_template(messages, add_generation_prompt=True) for messages in messages_list]
-
-sampling_params.stop = [tokenizer.eos_token]
-outputs = llm.generate(prompt_token_ids=prompt_token_ids, sampling_params=sampling_params)
-
-generated_text = [output.outputs[0].text for output in outputs]
-print(generated_text)
-```
-
 ## 6. FAQ
 
 ### Could You Provide the tokenizer.model File for Model Quantization?