code sample

README.md

@@ -9,15 +9,33 @@ tags:
 
 # codellama-abliterated
 
-CodeLlama-7v-Instruct-hf adapted using the abliteration notebook from [Maxime Labonne's LLM Course](https://github.com/mlabonne/llm-course)
+CodeLlama-7b-Instruct-hf adapted using the abliteration notebook from [Maxime Labonne's LLM Course](https://github.com/mlabonne/llm-course)
 
 Based on the paper ["Refusal in Language Models Is Mediated by a Single Direction"](https://arxiv.org/abs/2406.11717)
 
-Note that the CodeLlama series is based on Llama v2, from summer 2023.
+**Based on CodeLlama/Llama2 and subject to the restrictions of that model and license - not for unapproved uses**: 
 
 ## Concept
 
 There are hundreds of "abliterated" models on HuggingFace, using safety prompt datasets to edit a model and remove safety-tuning methods.
 
 None of these abliterated models have explored code LLMs, code-generation, and CyberSecEval. I don't know a lot about how well these will
-work, but this is a first step.
+work, but this is a first step.
+
+Blog: https://huggingface.co/blog/monsoon-nlp/refusal-in-code-llms
+
+## Usage
+
+```python
+! pip install transformers accelerate --quiet
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer, AutoConfig
+
+tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLlama-7b-Instruct-hf")
+model = AutoModelForCausalLM.from_pretrained("monsoon-nlp/codellama-abliterated", device_map="auto")
+
+code_generator = pipeline('text-generation', model=model, tokenizer=tokenizer, do_sample=False)
+
+input_string = "[INST] Write a python function to calculate the factorial of a number [/INST]"
+generated_code = code_generator(input_string, max_length=100)[0]['generated_text']
+print(generated_code)
+```