Update README.md
Browse files
README.md
CHANGED
@@ -24,13 +24,65 @@ datasets:
|
|
24 |
|
25 |
# Model Card for peleke-phi-4
|
26 |
|
27 |
-
This model is a fine-tuned version of [microsoft/phi-4](https://huggingface.co/microsoft/phi-4).
|
28 |
-
It
|
29 |
|
30 |
## Quick start
|
31 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
32 |
```python
|
33 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
34 |
```
|
35 |
|
36 |
## Training procedure
|
|
|
24 |
|
25 |
# Model Card for peleke-phi-4
|
26 |
|
27 |
+
This model is a fine-tuned version of [microsoft/phi-4](https://huggingface.co/microsoft/phi-4) for antibody sequence generation.
|
28 |
+
It takes in an antigen sequence, and returns novel Fv portions of heavy and light chain antibody sequences.
|
29 |
|
30 |
## Quick start
|
31 |
|
32 |
+
1. Load in the Model
|
33 |
+
|
34 |
+
```python
|
35 |
+
model_name = 'silicobio/peleke-phi-4'
|
36 |
+
config = PeftConfig.from_pretrained(model_name)
|
37 |
+
|
38 |
+
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
|
39 |
+
|
40 |
+
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, torch_dtype=torch.bfloat16, trust_remote_code=True).cuda()
|
41 |
+
model.resize_token_embeddings(len(tokenizer))
|
42 |
+
model = PeftModel.from_pretrained(model, model_name).cuda()
|
43 |
+
```
|
44 |
+
|
45 |
+
2. Format your Input
|
46 |
+
|
47 |
+
This model uses `<epi>` and `</epi>` to annotate epitope residues of interest.
|
48 |
+
|
49 |
+
It may be easier to use other characters for annotation, such as `[ ]`'s. For example: `...CSFS[S][F][V]L[N]WY...`.
|
50 |
+
Then, use the following function to properly format the input.
|
51 |
+
|
52 |
```python
|
53 |
+
def format_prompt(antigen_sequence):
|
54 |
+
epitope_seq = re.sub(r'\[([A-Z])\]', r'<epi>\1</epi>', antigen_sequence)
|
55 |
+
formatted_str = f"Antigen: {epitope_seq}<|im_end|>\nAntibody:"
|
56 |
+
return formatted_str
|
57 |
+
```
|
58 |
+
|
59 |
+
3. Generate an Antibody Sequence
|
60 |
+
|
61 |
+
```python
|
62 |
+
prompt = format_prompt(antigen)
|
63 |
+
inputs = tokenizer(prompt, return_tensors="pt")
|
64 |
+
inputs = {k: v.cuda() for k, v in inputs.items()}
|
65 |
+
|
66 |
+
with torch.no_grad():
|
67 |
+
outputs = model.generate(
|
68 |
+
**inputs,
|
69 |
+
max_new_tokens=1000,
|
70 |
+
do_sample=True,
|
71 |
+
temperature=0.7,
|
72 |
+
pad_token_id=tokenizer.eos_token_id,
|
73 |
+
use_cache=False,
|
74 |
+
)
|
75 |
+
|
76 |
+
full_text = tokenizer.decode(outputs[0], skip_special_tokens=False)
|
77 |
+
antibody_sequence = full_text.split('<|im_end|>')[1].replace('Antibody: ', '')
|
78 |
+
print(f"Antigen: {antigen}\nAntibody: {antibody_sequence}\n")
|
79 |
+
```
|
80 |
+
|
81 |
+
This will generate a `|`-delimited output, which is an Fv portion of a heavy and light chain.
|
82 |
+
|
83 |
+
```sh
|
84 |
+
Antigen: NPPTFSPALL...
|
85 |
+
Antibody: QVQLVQSGGG...|DIQMTQSPSS...
|
86 |
```
|
87 |
|
88 |
## Training procedure
|