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import torch
import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_ID = "Shekswess/trlm-135m"
# Load tokenizer & model
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
torch_dtype=torch.float16 if device == "cuda" else torch.float32,
)
model.to(device)
model.eval()
def generate_reply(prompt, max_new_tokens, temperature, top_p):
if not prompt.strip():
return ""
# Use the model's chat template (as in the README)
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
inputs = tokenizer(text, return_tensors="pt").to(device)
with torch.no_grad():
output_ids = model.generate(
**inputs,
max_new_tokens=int(max_new_tokens),
do_sample=True,
temperature=float(temperature),
top_p=float(top_p),
pad_token_id=tokenizer.eos_token_id,
)
# Drop the prompt tokens and decode only the completion
generated_ids = output_ids[0, inputs["input_ids"].shape[1]:]
decoded = tokenizer.decode(generated_ids, skip_special_tokens=True)
return decoded.strip()
with gr.Blocks() as demo:
gr.Markdown("# Tiny Reasoning LM (trlm-135m)\nSmall 135M reasoning model by **Shekswess**.")
with gr.Row():
with gr.Column(scale=3):
prompt = gr.Textbox(
lines=8,
label="Prompt",
placeholder="Ask a question or give an instruction…",
)
max_new_tokens = gr.Slider(
minimum=16,
maximum=256,
value=128,
step=8,
label="Max new tokens",
)
temperature = gr.Slider(
minimum=0.1,
maximum=1.5,
value=0.8,
step=0.05,
label="Temperature",
)
top_p = gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.9,
step=0.05,
label="Top-p",
)
generate_btn = gr.Button("Generate")
with gr.Column(scale=4):
output = gr.Textbox(
lines=12,
label="Model Output",
)
generate_btn.click(
fn=generate_reply,
inputs=[prompt, max_new_tokens, temperature, top_p],
outputs=[output],
)
if __name__ == "__main__":
demo.launch() |