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da6e4b6 d1cb365 da6e4b6 4e8b102 00bf8ab 4e8b102 da6e4b6 4e8b102 da6e4b6 ef825c7 da6e4b6 b13d266 da6e4b6 b13d266 da6e4b6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
BASE_MODEL = "meta-llama/Llama-3.2-3B-Instruct"
ADAPTER = "iamcodio/codio-rogerian-v1"
model = None
tokenizer = None
def load_model():
global model, tokenizer
if model is None:
tokenizer = AutoTokenizer.from_pretrained(ADAPTER)
base = AutoModelForCausalLM.from_pretrained(
BASE_MODEL, torch_dtype=torch.float16, device_map="auto"
)
model = PeftModel.from_pretrained(base, ADAPTER)
model.eval()
@spaces.GPU
def respond(message, history):
load_model()
messages = []
for turn in history:
if isinstance(turn, dict):
messages.append({"role": turn["role"], "content": turn["content"]})
elif isinstance(turn, (list, tuple)) and len(turn) == 2:
messages.append({"role": "user", "content": turn[0]})
if turn[1]:
messages.append({"role": "assistant", "content": turn[1]})
messages.append({"role": "user", "content": message})
tokenized = tokenizer.apply_chat_template(
messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True
)
input_ids = tokenized["input_ids"].to(model.device)
attention_mask = tokenized["attention_mask"].to(model.device)
with torch.no_grad():
outputs = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
max_new_tokens=300,
temperature=0.7,
top_p=0.9,
do_sample=True,
repetition_penalty=1.1,
)
raw = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True)
# Strip Llama 3 structured output format if present
if isinstance(raw, str) and raw.startswith("[{"):
try:
import json
parsed = json.loads(raw)
if isinstance(parsed, list):
raw = " ".join(item.get("text", "") for item in parsed if isinstance(item, dict))
except (json.JSONDecodeError, TypeError):
pass
return raw.strip()
demo = gr.ChatInterface(
fn=respond,
title="BrainFart — Rogerian Listening Model",
description="Fine-tuned Llama 3.2 3B on therapeutic conversation data. Not a therapist — just here to listen.",
examples=["I've been feeling really overwhelmed lately and I don't know why",
"I think I'm stuck in a cycle of self-sabotage",
"Everyone keeps telling me to be positive but it's not that simple"],
)
demo.launch()
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