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f1673b8 e2932cc 82da8fb e2932cc f1673b8 e2932cc 56e556c ddde7bc 56e556c 6e1bd48 56e556c ddde7bc 56e556c f1673b8 | 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 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | import gradio as gr
import torch
import numpy as np
import struct
import lzma
import json
from huggingface_hub import hf_hub_download
from transformers import T5Config, T5ForConditionalGeneration, AutoTokenizer
# Download quantized model
model_path = hf_hub_download(repo_id="ag14850/Mosquito", filename="mosquito_tiny.bin.xz")
def unpack_nbits(data, bits, count):
if bits == 8:
return np.frombuffer(data, dtype=np.uint8)[:count]
result = []
if bits == 4:
for byte in data:
result.append((byte >> 4) & 0x0F)
result.append(byte & 0x0F)
elif bits == 6:
for i in range(0, len(data), 3):
if i + 2 >= len(data):
break
b0, b1, b2 = data[i], data[i+1], data[i+2]
result.append((b0 >> 2) & 0x3F)
result.append(((b0 & 0x03) << 4) | ((b1 >> 4) & 0x0F))
result.append(((b1 & 0x0F) << 2) | ((b2 >> 6) & 0x03))
result.append(b2 & 0x3F)
elif bits == 5:
for i in range(0, len(data), 5):
if i + 4 >= len(data):
break
packed = int.from_bytes(data[i:i+5], 'little')
for j in range(8):
result.append((packed >> (j * 5)) & 0x1F)
elif bits == 7:
for i in range(0, len(data), 7):
if i + 6 >= len(data):
break
packed = int.from_bytes(data[i:i+7], 'little')
for j in range(8):
result.append((packed >> (j * 7)) & 0x7F)
return np.array(result[:count], dtype=np.uint8)
def load_quantized_model(path):
with lzma.open(path, 'rb') as f:
data = f.read()
offset = 0
version, default_bits, num_params = struct.unpack_from('<BBH', data, offset)
offset += 4
state_dict = {}
for _ in range(num_params):
name_len = struct.unpack_from('<H', data, offset)[0]
offset += 2
name = data[offset:offset + name_len].decode('utf-8')
offset += name_len
ndim = struct.unpack_from('<B', data, offset)[0]
offset += 1
shape = tuple(struct.unpack_from('<I', data, offset + i*4)[0] for i in range(ndim))
offset += ndim * 4
numel = int(np.prod(shape)) if shape else 1
bits = struct.unpack_from('<B', data, offset)[0]
offset += 1
if bits < 16:
scale, zp = struct.unpack_from('<ff', data, offset)
offset += 8
packed_len = struct.unpack_from('<I', data, offset)[0]
offset += 4
packed_data = data[offset:offset + packed_len]
offset += packed_len
quantized = unpack_nbits(packed_data, bits, numel)
tensor_data = ((quantized.astype(np.float32) - zp) * scale).reshape(shape)
state_dict[name] = torch.from_numpy(tensor_data)
else:
fp16_len = struct.unpack_from('<I', data, offset)[0]
offset += 4
fp16_data = data[offset:offset + fp16_len]
offset += fp16_len
tensor_data = np.frombuffer(fp16_data, dtype=np.float16).reshape(shape)
state_dict[name] = torch.from_numpy(tensor_data.astype(np.float32))
config_len = struct.unpack_from('<I', data, offset)[0]
offset += 4
config_json = data[offset:offset + config_len].decode('utf-8')
config = T5Config.from_dict(json.loads(config_json))
model = T5ForConditionalGeneration(config)
model.load_state_dict(state_dict)
model.eval()
return model
# Load model
model = load_quantized_model(model_path)
tokenizer = AutoTokenizer.from_pretrained("google/t5-v1_1-base", legacy=False)
def ask(question):
inputs = tokenizer(f"question: {question}", return_tensors="pt", max_length=128, truncation=True)
outputs = model.generate(
**inputs,
max_new_tokens=24,
num_beams=6,
no_repeat_ngram_size=2,
repetition_penalty=20.0,
early_stopping=True
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Sample Q&A to display
sample_qa = """
## 📊 Sample Questions & Answers
| Question | Answer |
|----------|--------|
| How do vaccines work? | Vaccines stimulate the immune system to recognize and fight specific pathogens. |
| Why do we sneeze? | Sneezes clear irritants from the nasal passages. |
| What is empathy? | Empathy is the ability to understand and share the feelings of another person. |
"""
with gr.Blocks() as demo:
gr.Markdown("# 🦟 Mosquito - Tiny Knowledge Model")
gr.Markdown("A **7.3M parameter** model that answers general knowledge questions. Smaller than a mosquito's brain!")
gr.Markdown(sample_qa)
gr.Markdown("---")
gr.Markdown("## Try it yourself:")
with gr.Row():
question = gr.Textbox(label="Question", placeholder="Why do we dream?")
answer = gr.Textbox(label="Answer")
submit_btn = gr.Button("Ask", variant="primary")
submit_btn.click(fn=ask, inputs=question, outputs=answer)
gr.Examples(
examples=[
["How do vaccines work?"],
["Why do we sneeze?"],
["What is empathy?"],
["Why is the sky blue?"],
["What causes earthquakes?"],
],
inputs=question,
)
demo.launch() |