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  # Model Card for peleke-phi-4
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- This model is a fine-tuned version of [microsoft/phi-4](https://huggingface.co/microsoft/phi-4).
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- It has been trained using [TRL](https://github.com/huggingface/trl).
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  ## Quick start
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  ```python
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- # Coming Soon...
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ```
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  ## Training procedure
 
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  # Model Card for peleke-phi-4
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+ This model is a fine-tuned version of [microsoft/phi-4](https://huggingface.co/microsoft/phi-4) for antibody sequence generation.
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+ It takes in an antigen sequence, and returns novel Fv portions of heavy and light chain antibody sequences.
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  ## Quick start
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+ 1. Load in the Model
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+
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+ ```python
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+ model_name = 'silicobio/peleke-phi-4'
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+ config = PeftConfig.from_pretrained(model_name)
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
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+
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+ model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, torch_dtype=torch.bfloat16, trust_remote_code=True).cuda()
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+ model.resize_token_embeddings(len(tokenizer))
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+ model = PeftModel.from_pretrained(model, model_name).cuda()
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+ ```
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+
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+ 2. Format your Input
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+
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+ This model uses `<epi>` and `</epi>` to annotate epitope residues of interest.
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+
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+ It may be easier to use other characters for annotation, such as `[ ]`'s. For example: `...CSFS[S][F][V]L[N]WY...`.
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+ Then, use the following function to properly format the input.
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+
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  ```python
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+ def format_prompt(antigen_sequence):
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+ epitope_seq = re.sub(r'\[([A-Z])\]', r'<epi>\1</epi>', antigen_sequence)
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+ formatted_str = f"Antigen: {epitope_seq}<|im_end|>\nAntibody:"
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+ return formatted_str
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+ ```
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+
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+ 3. Generate an Antibody Sequence
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+
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+ ```python
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+ prompt = format_prompt(antigen)
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+ inputs = tokenizer(prompt, return_tensors="pt")
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+ inputs = {k: v.cuda() for k, v in inputs.items()}
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+
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+ with torch.no_grad():
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+ outputs = model.generate(
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+ **inputs,
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+ max_new_tokens=1000,
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+ do_sample=True,
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+ temperature=0.7,
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+ pad_token_id=tokenizer.eos_token_id,
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+ use_cache=False,
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+ )
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+
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+ full_text = tokenizer.decode(outputs[0], skip_special_tokens=False)
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+ antibody_sequence = full_text.split('<|im_end|>')[1].replace('Antibody: ', '')
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+ print(f"Antigen: {antigen}\nAntibody: {antibody_sequence}\n")
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+ ```
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+
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+ This will generate a `|`-delimited output, which is an Fv portion of a heavy and light chain.
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+
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+ ```sh
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+ Antigen: NPPTFSPALL...
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+ Antibody: QVQLVQSGGG...|DIQMTQSPSS...
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  ```
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  ## Training procedure