Update README.md

README.md

@@ -43,7 +43,7 @@ pip install transformers
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-checkpoint = "HuggingFaceTB/SmolLM2-135M-Instruct"
+checkpoint = "AssistantsLab/SmolLM2-135M-humanized"
 
 device = "cuda" # for GPU usage or "cpu" for CPU usage
 tokenizer = AutoTokenizer.from_pretrained(checkpoint)
@@ -62,7 +62,7 @@ print(tokenizer.decode(outputs[0]))
 You can also use the TRL CLI to chat with the model from the terminal:
 ```bash
 pip install trl
-trl chat --model_name_or_path HuggingFaceTB/SmolLM2-135M-Instruct --device cpu
+trl chat --model_name_or_path AssistantsLab/SmolLM2-135M-humanized --device cpu
 ```
 
 ## Evaluation
@@ -90,21 +90,7 @@ In this section, we report the evaluation results of SmolLM2. All evaluations ar
 
 SmolLM2 models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content.
 
-## Training
-
-### Model
-
-- **Architecture:** Transformer decoder
-- **Pretraining tokens:** 2T
-- **Precision:** bfloat16
-
-### Hardware
-
-- **GPUs:** 64 H100
-
-### Software
-
-- **Training Framework:** [nanotron](https://github.com/huggingface/nanotron/tree/main)
+Humanized models display a bigger preference for confident hallucinating in some limited testing. Please keep this in mind in any potential applications.
 
 ## License