Update README.md

README.md

@@ -39,8 +39,6 @@ GPTQ used a 1% damping factor and 256 sequences of 8,192 random tokens.
 
 ## Deployment
 
-### Use with vLLM
-
 This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
 
 ```python
@@ -71,50 +69,6 @@ print(generated_text)
 
 vLLM aslo supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
 
-### Use with transformers
-
-The following example contemplates how the model can be deployed in Transformers using the `generate()` function.
-
-
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-
-model_id = "neuralmagic/Meta-Llama-3.1-8B-Instruct-quantized.w8a8"
-
-tokenizer = AutoTokenizer.from_pretrained(model_id)
-model = AutoModelForCausalLM.from_pretrained(
-    model_id,
-    torch_dtype="auto",
-    device_map="auto",
-)
-
-messages = [
-    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
-    {"role": "user", "content": "Who are you?"},
-]
-
-input_ids = tokenizer.apply_chat_template(
-    messages,
-    add_generation_prompt=True,
-    return_tensors="pt"
-).to(model.device)
-
-terminators = [
-    tokenizer.eos_token_id,
-    tokenizer.convert_tokens_to_ids("<|eot_id|>")
-]
-
-outputs = model.generate(
-    input_ids,
-    max_new_tokens=256,
-    eos_token_id=terminators,
-    do_sample=True,
-    temperature=0.6,
-    top_p=0.9,
-)
-response = outputs[0][input_ids.shape[-1]:]
-print(tokenizer.decode(response, skip_special_tokens=True))
-```
 
 ## Creation