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README.md

@@ -17,42 +17,6 @@ base_model: HuggingFaceTB/SmolLM2-135M-Instruct
 This model was converted to GGUF format from [`HuggingFaceTB/SmolLM2-135M-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct) for more details on the model.
 
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-Model details:
--
-SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device.
-
-SmolLM2 demonstrates significant advances over its predecessor SmolLM1, particularly in instruction following, knowledge, reasoning. The 135M model was trained on 2 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own curated datasets. We then applied Direct Preference Optimization (DPO) using UltraFeedback.
-
-The instruct model additionally supports tasks such as text rewriting, summarization and function calling (for the 1.7B) thanks to datasets developed by Argilla such as Synth-APIGen-v0.1. You can find the SFT dataset here: https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk and finetuning code at https://github.com/huggingface/alignment-handbook/tree/main/recipes/smollm2
-How to use
-Transformers
-
-pip install transformers
-
-from transformers import AutoModelForCausalLM, AutoTokenizer
-checkpoint = "HuggingFaceTB/SmolLM2-135M-Instruct"
-
-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)
-
-messages = [{"role": "user", "content": "What is gravity?"}]
-input_text=tokenizer.apply_chat_template(messages, tokenize=False)
-print(input_text)
-inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)
-outputs = model.generate(inputs, max_new_tokens=50, temperature=0.2, top_p=0.9, do_sample=True)
-print(tokenizer.decode(outputs[0]))
-
-Chat in TRL
-
-You can also use the TRL CLI to chat with the model from the terminal:
-
-pip install trl
-trl chat --model_name_or_path HuggingFaceTB/SmolLM2-135M-Instruct --device cpu
-
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 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)