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c11a360 6373094 c11a360 dc15b97 c11a360 dc15b97 6373094 dc15b97 6373094 dc15b97 c11a360 dc15b97 c11a360 6373094 c11a360 6373094 c11a360 dc15b97 c11a360 dc15b97 | 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 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
base_model_id = "google/gemma-2b-it"
adapter_model_id = "MatclassAI/Matclass-Gemma-2b-v1"
print("Loading base tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(adapter_model_id)
print("Loading base framework layers...")
model = AutoModelForCausalLM.from_pretrained(
base_model_id,
torch_dtype=torch.float16,
device_map="auto",
low_cpu_mem_usage=True
)
print("Injecting custom fine-tuned adapters into core matrices...")
model.load_adapter(adapter_model_id)
print("Model infrastructure fully compiled!")
def generate_text(prompt, system_prompt, temperature, max_tokens):
if system_prompt:
formatted_prompt = f"<start_of_turn>user\n{system_prompt}\n\n{prompt}<end_of_turn>\n<start_of_turn>model\n"
else:
formatted_prompt = f"<start_of_turn>user\n{prompt}<end_of_turn>\n<start_of_turn>model\n"
inputs = tokenizer(formatted_prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=max_tokens,
do_sample=True,
temperature=temperature,
top_k=50,
top_p=0.95
)
generated = tokenizer.decode(outputs[0], skip_special_tokens=False)
if formatted_prompt in generated:
return generated.split("<start_of_turn>model\n")[-1].replace("<end_of_turn>", "").strip()
return generated
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# Matclass AI - Gemma 2B Adapter Workspace")
gr.Markdown("Dynamic LoRA adapter execution layer tracking custom weights.")
with gr.Row():
with gr.Column(scale=2):
system_input = gr.Textbox(
label="System Instructions",
placeholder="Define assistant behavior or context here...",
lines=2
)
user_input = gr.Textbox(
label="Query",
placeholder="Ask anything...",
lines=5
)
submit_btn = gr.Button("Generate Text", variant="primary")
with gr.Column(scale=1):
gr.Markdown("### Generation Hyperparameters")
temp_slider = gr.Slider(
minimum=0.1,
maximum=1.5,
value=0.7,
step=0.05,
label="Temperature"
)
tokens_slider = gr.Slider(
minimum=64,
maximum=1024,
value=256,
step=64,
label="Max New Tokens"
)
with gr.Row():
output_text = gr.Textbox(label="Model Output", lines=8, interactive=False)
submit_btn.click(
fn=generate_text,
inputs=[user_input, system_input, temp_slider, tokens_slider],
outputs=output_text
)
if __name__ == "__main__":
demo.launch()
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