Spaces:
Running
on
Zero
Running
on
Zero
File size: 9,694 Bytes
1a22b81 08a1b37 1a22b81 4706d9e 1a22b81 08a1b37 4706d9e 1a22b81 4706d9e 1a22b81 7786282 8e79c3b 7786282 8e79c3b 7786282 8e79c3b 7786282 8e79c3b 7786282 8e79c3b 7786282 8e79c3b 7786282 8e79c3b 1a22b81 08a1b37 1a22b81 08a1b37 1a22b81 0077bf2 1a22b81 08a1b37 1a22b81 0077bf2 1a22b81 08a1b37 1a22b81 8e79c3b 1ee8b3b 8e79c3b 1a22b81 8e79c3b 4706d9e 209496a 4706d9e 209496a 4706d9e 7786282 8e79c3b 4706d9e 209496a 1a22b81 8e79c3b 1a22b81 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 |
import gradio as gr
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "rubenroy/Zurich-14B-GCv2-5m"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
@spaces.GPU
def generate(message, chat_history, temperature=0.7, top_p=0.9, top_k=50, max_new_tokens=512, repetition_penalty=1.1):
messages = [
{"role": "system", "content": "You are Zurich, a 14 billion parameter Large Language model built on the Qwen 2.5 14B model developed by Alibaba Cloud, and fine-tuned by Ruben Roy. You have been fine-tuned with the GammaCorpus v2 dataset, a dataset filled with structured and filtered multi-turn conversations and was also created by Ruben Roy. You are a helpful assistant."},
{"role": "user", "content": message}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
temperature=float(temperature),
top_p=float(top_p),
top_k=int(top_k),
max_new_tokens=int(max_new_tokens),
repetition_penalty=float(repetition_penalty),
do_sample=True if float(temperature) > 0 else False
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
return response
TITLE_HTML = """
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css">
<style>
.model-btn {
background: linear-gradient(135deg, #2563eb 0%, #1d4ed8 100%);
color: white !important;
padding: 0.75rem 1rem;
border-radius: 0.5rem;
text-decoration: none !important;
font-weight: 500;
transition: all 0.2s ease;
font-size: 0.9rem;
display: flex;
align-items: center;
justify-content: center;
box-shadow: 0 2px 4px rgba(0,0,0,0.1);
}
.model-btn:hover {
background: linear-gradient(135deg, #1d4ed8 0%, #1e40af 100%);
box-shadow: 0 4px 6px rgba(0,0,0,0.2);
}
.model-section {
flex: 1;
max-width: 450px;
background: rgba(255, 255, 255, 0.05);
padding: 1.5rem;
border-radius: 1rem;
border: 1px solid rgba(255, 255, 255, 0.1);
backdrop-filter: blur(10px);
transition: all 0.3s ease;
}
.info-link {
color: #60a5fa;
text-decoration: none;
transition: color 0.2s ease;
}
.info-link:hover {
color: #93c5fd;
text-decoration: underline;
}
.info-section {
margin-top: 0.5rem;
font-size: 0.9rem;
color: #94a3b8;
}
.settings-section {
background: rgba(255, 255, 255, 0.05);
padding: 1.5rem;
border-radius: 1rem;
margin: 1.5rem auto;
border: 1px solid rgba(255, 255, 255, 0.1);
max-width: 800px;
}
.settings-title {
color: #e2e8f0;
font-size: 1.25rem;
font-weight: 600;
margin-bottom: 1rem;
display: flex;
align-items: center;
gap: 0.7rem;
}
.parameter-info {
color: #94a3b8;
font-size: 0.8rem;
margin-top: 0.25rem;
}
</style>
<div style="background: linear-gradient(135deg, #1e293b 0%, #0f172a 100%); padding: 1.5rem; border-radius: 1.5rem; text-align: center; margin: 1rem auto; max-width: 1200px; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);">
<div style="margin-bottom: 1.5rem;">
<div style="display: flex; align-items: center; justify-content: center; gap: 1rem;">
<h1 style="font-size: 2.5rem; font-weight: 800; margin: 0; background: linear-gradient(135deg, #60a5fa 0%, #93c5fd 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">Zurich</h1>
<div style="width: 2px; height: 2.5rem; background: linear-gradient(180deg, #3b82f6 0%, #60a5fa 100%);"></div>
<p style="font-size: 1.25rem; color: #94a3b8; margin: 0;">GammaCorpus v2-5m</p>
</div>
<div class="info-section">
<span>Fine-tuned from <a href="https://huggingface.co/Qwen/Qwen2.5-14B-Instruct" class="info-link">Qwen 2.5 14B Instruct</a> | Model: <a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-5m" class="info-link">Zurich-14B-GCv2-5m</a> | Training Dataset: <a href="https://huggingface.co/datasets/rubenroy/GammaCorpus-v2-5m" class="info-link">GammaCorpus v2 5m</a></span>
</div>
</div>
<div style="display: flex; gap: 1.5rem; justify-content: center;">
<div class="model-section">
<h2 style="font-size: 1.25rem; color: #e2e8f0; margin-bottom: 1.4rem; margin-top: 1px; font-weight: 600; display: flex; align-items: center; justify-content: center; gap: 0.7rem;">
<i class="fas fa-brain"></i>
7B Models
</h2>
<div style="display: grid; grid-template-columns: repeat(2, 1fr); gap: 0.75rem;">
<a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-5m" class="model-btn">Zurich 7B GCv2 5m</a>
<a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-1m" class="model-btn">Zurich 7B GCv2 1m</a>
<a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-500k" class="model-btn">Zurich 7B GCv2 500k</a>
<a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-100k" class="model-btn">Zurich 7B GCv2 100k</a>
<a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-50k" class="model-btn">Zurich 7B GCv2 50k</a>
<a href="https://huggingface.co/sces/rubenroy/Zurich-7B-GCv2-10k" class="model-btn">Zurich 7B GCv2 10k</a>
</div>
</div>
<div class="model-section">
<h2 style="font-size: 1.25rem; color: #e2e8f0; margin-bottom: 1.4rem; margin-top: 1px; font-weight: 600; display: flex; align-items: center; justify-content: center; gap: 0.7rem;">
<i class="fas fa-rocket"></i>
14B Models
</h2>
<div style="display: grid; grid-template-columns: repeat(2, 1fr); gap: 0.75rem;">
<a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-5m" class="model-btn">Zurich 14B GCv2 5m</a>
<a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-1m" class="model-btn">Zurich 14B GCv2 1m</a>
<a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-500k" class="model-btn">Zurich 14B GCv2 500k</a>
<a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-100k" class="model-btn">Zurich 14B GCv2 100k</a>
<a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-50k" class="model-btn">Zurich 14B GCv2 50k</a>
<a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-10k" class="model-btn">Zurich 14B GCv2 10k</a>
</div>
</div>
</div>
</div>
"""
examples = [
["Explain quantum computing in simple terms"],
["Write a short story about a time traveler"],
["Explain the process of photosynthesis"],
["Tell me an intersting fact about Palm trees"]
]
with gr.Blocks() as demo:
gr.HTML(TITLE_HTML)
with gr.Accordion("Generation Settings", open=False):
with gr.Row():
with gr.Column():
temperature = gr.Slider(
minimum=0.0,
maximum=2.0,
value=0.7,
step=0.1,
label="Temperature",
info="Higher values make the output more random, lower values make it more deterministic",
interactive=True
)
top_p = gr.Slider(
minimum=0.0,
maximum=1.0,
value=0.9,
step=0.05,
label="Top P",
info="Controls the cumulative probability threshold for nucleus sampling",
interactive=True
)
top_k = gr.Slider(
minimum=1,
maximum=100,
value=50,
step=1,
label="Top K",
info="Limits the number of tokens to consider for each generation step",
interactive=True
)
with gr.Column():
max_new_tokens = gr.Slider(
minimum=1,
maximum=2048,
value=512,
step=1,
label="Max New Tokens",
info="Maximum number of tokens to generate in the response",
interactive=True
)
repetition_penalty = gr.Slider(
minimum=1.0,
maximum=2.0,
value=1.1,
step=0.1,
label="Repetition Penalty",
info="Higher values stop the model from repeating the same info",
interactive=True
)
chatbot = gr.ChatInterface(
fn=generate,
additional_inputs=[
temperature,
top_p,
top_k,
max_new_tokens,
repetition_penalty
],
examples=examples
)
demo.launch(share=True) |