Update README.md

README.md

@@ -29,70 +29,6 @@ To build SmolLM-Instruct, we instruction tuned the models using publicly availab
 
 This is the SmolLM-360M-Instruct.
 
-### Generation
-```bash
-pip install transformers
-```
-
-#### Running the model on CPU/GPU/multi GPU
-* _Using full precision_
-```python
-# pip install git+https://github.com/huggingface/transformers.git # TODO: merge PR to main
-from transformers import AutoModelForCausalLM, AutoTokenizer
-checkpoint = "HuggingFaceTB/SmolLM-135M"
-device = "cuda" # for GPU usage or "cpu" for CPU usage
-tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-# for multiple GPUs install accelerate and do `model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto")`
-model = AutoModelForCausalLM.from_pretrained(checkpoint).to(device)
-inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
-outputs = model.generate(inputs)
-print(tokenizer.decode(outputs[0]))
-```
-```bash
->>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
-Memory footprint: 12624.81 MB
-```
-* _Using `torch.bfloat16`_
-```python
-# pip install accelerate
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-checkpoint = "HuggingFaceTB/SmolLM-135M"
-tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-# for fp16 use `torch_dtype=torch.float16` instead
-model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", torch_dtype=torch.bfloat16)
-inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda")
-outputs = model.generate(inputs)
-print(tokenizer.decode(outputs[0]))
-```
-```bash
->>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
-Memory footprint: 269.03 MB
-```
-
-#### Quantized Versions through `bitsandbytes`
-* _Using 8-bit precision (int8)_
-
-```python
-# pip install bitsandbytes accelerate
-from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
-# to use 4bit use `load_in_4bit=True` instead
-quantization_config = BitsAndBytesConfig(load_in_8bit=True)
-checkpoint = "HuggingFaceTB/SmolLM-135M"
-tokenizer = AutoTokenizer.from_pretrained(checkpoint)
-model = AutoModelForCausalLM.from_pretrained(checkpoint, quantization_config=quantization_config)
-inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to("cuda")
-outputs = model.generate(inputs)
-print(tokenizer.decode(outputs[0]))
-```
-```bash
->>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
-# load_in_8bit
-Memory footprint: 162.87 MB
-# load_in_4bit
->>> print(f"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB")
-Memory footprint: 109.78 MB
-```
 
 # Limitations