Update README.md

README.md

@@ -18,6 +18,34 @@ base_model: huihui-ai/SmolLM2-1.7B-Instruct-abliterated
 This model was converted to GGUF format from [`huihui-ai/SmolLM2-1.7B-Instruct-abliterated`](https://huggingface.co/huihui-ai/SmolLM2-1.7B-Instruct-abliterated) 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/huihui-ai/SmolLM2-1.7B-Instruct-abliterated) for more details on the model.
 
+---
+Model details:
+-
+This is an uncensored version of HuggingFaceTB/SmolLM2-1.7B-Instruct created with abliteration (see remove-refusals-with-transformers to know more about it).
+
+If the desired result is not achieved, you can clear the conversation and try again.
+
+How to use
+-
+Transformers
+
+pip install transformers
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+checkpoint = "huihui-ai/SmolLM2-1.7B-Instruct-abliterated"
+
+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 the capital of France."}]
+input_text=tokenizer.apply_chat_template(messages, tokenize=False)
+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]))
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)