import gradio as gr
import gemini_gradio
import openai_gradio
import anthropic_gradio
import sambanova_gradio
import xai_gradio
import hyperbolic_gradio
import perplexity_gradio
import mistral_gradio
import fireworks_gradio
import cerebras_gradio
import groq_gradio
import together_gradio
import nvidia_gradio
import dashscope_gradio
with gr.Blocks(fill_height=True) as demo:
with gr.Tab("Meta Llama"):
with gr.Row():
llama_model = gr.Dropdown(
choices=[
'Meta-Llama-3.2-1B-Instruct', # Llama 3.2 1B
'Meta-Llama-3.2-3B-Instruct', # Llama 3.2 3B
'Llama-3.2-11B-Vision-Instruct', # Llama 3.2 11B
'Llama-3.2-90B-Vision-Instruct', # Llama 3.2 90B
'Meta-Llama-3.1-8B-Instruct', # Llama 3.1 8B
'Meta-Llama-3.1-70B-Instruct', # Llama 3.1 70B
'Meta-Llama-3.1-405B-Instruct' # Llama 3.1 405B
],
value='Llama-3.2-90B-Vision-Instruct', # Default to the most advanced model
label="Select Llama Model",
interactive=True
)
llama_interface = gr.load(
name=llama_model.value,
src=sambanova_gradio.registry,
multimodal=True,
fill_height=True
)
def update_llama_model(new_model):
return gr.load(
name=new_model,
src=sambanova_gradio.registry,
multimodal=True,
fill_height=True
)
llama_model.change(
fn=update_llama_model,
inputs=[llama_model],
outputs=[llama_interface]
)
gr.Markdown("**Note:** You need to use a SambaNova API key from [SambaNova Cloud](https://cloud.sambanova.ai/).")
with gr.Tab("Gemini"):
with gr.Row():
gemini_model = gr.Dropdown(
choices=[
'gemini-1.5-flash', # Fast and versatile performance
'gemini-1.5-flash-8b', # High volume, lower intelligence tasks
'gemini-1.5-pro', # Complex reasoning tasks
'gemini-exp-1114' # Quality improvements
],
value='gemini-1.5-pro', # Default to the most advanced model
label="Select Gemini Model",
interactive=True
)
gemini_interface = gr.load(
name=gemini_model.value,
src=gemini_gradio.registry,
fill_height=True
)
def update_gemini_model(new_model):
return gr.load(
name=new_model,
src=gemini_gradio.registry,
fill_height=True
)
gemini_model.change(
fn=update_gemini_model,
inputs=[gemini_model],
outputs=[gemini_interface]
)
with gr.Tab("ChatGPT"):
with gr.Row():
model_choice = gr.Dropdown(
choices=[
'gpt-4o-2024-11-20', # Latest GPT-4o model
'gpt-4o', # Previous most advanced model
'gpt-4o-2024-08-06', # Latest snapshot
'gpt-4o-2024-05-13', # Original snapshot
'chatgpt-4o-latest', # Latest ChatGPT version
'gpt-4o-mini', # Small model
'gpt-4o-mini-2024-07-18', # Latest mini version
'o1-preview', # Reasoning model
'o1-preview-2024-09-12', # Latest o1 model snapshot
'o1-mini', # Faster reasoning model
'o1-mini-2024-09-12', # Latest o1-mini model snapshot
'gpt-4-turbo', # Latest GPT-4 Turbo model
'gpt-4-turbo-2024-04-09', # Latest GPT-4 Turbo snapshot
'gpt-4-turbo-preview', # GPT-4 Turbo preview model
'gpt-4-0125-preview', # GPT-4 Turbo preview model for laziness
'gpt-4-1106-preview', # Improved instruction following model
'gpt-4', # Standard GPT-4 model
'gpt-4-0613' # Snapshot of GPT-4 from June 2023
],
value='gpt-4o-2024-11-20', # Updated default to latest model
label="Select Model",
interactive=True
)
chatgpt_interface = gr.load(
name=model_choice.value,
src=openai_gradio.registry,
fill_height=True
)
def update_model(new_model):
return gr.load(
name=new_model,
src=openai_gradio.registry,
fill_height=True
)
model_choice.change(
fn=update_model,
inputs=[model_choice],
outputs=[chatgpt_interface]
)
with gr.Tab("Claude"):
with gr.Row():
claude_model = gr.Dropdown(
choices=[
'claude-3-5-sonnet-20241022', # Latest Sonnet
'claude-3-5-haiku-20241022', # Latest Haiku
'claude-3-opus-20240229', # Opus
'claude-3-sonnet-20240229', # Previous Sonnet
'claude-3-haiku-20240307' # Previous Haiku
],
value='claude-3-5-sonnet-20241022', # Default to latest Sonnet
label="Select Model",
interactive=True
)
claude_interface = gr.load(
name=claude_model.value,
src=anthropic_gradio.registry,
accept_token=True,
fill_height=True
)
def update_claude_model(new_model):
return gr.load(
name=new_model,
src=anthropic_gradio.registry,
accept_token=True,
fill_height=True
)
claude_model.change(
fn=update_claude_model,
inputs=[claude_model],
outputs=[claude_interface]
)
with gr.Tab("Grok"):
with gr.Row():
grok_model = gr.Dropdown(
choices=[
'grok-beta',
'grok-vision-beta'
],
value='grok-vision-beta',
label="Select Grok Model",
interactive=True
)
grok_interface = gr.load(
name=grok_model.value,
src=xai_gradio.registry,
fill_height=True
)
def update_grok_model(new_model):
return gr.load(
name=new_model,
src=xai_gradio.registry,
fill_height=True
)
grok_model.change(
fn=update_grok_model,
inputs=[grok_model],
outputs=[grok_interface]
)
with gr.Tab("Hugging Face"):
with gr.Row():
hf_model = gr.Dropdown(
choices=[
# Latest Large Models
'Qwen/Qwen2.5-Coder-32B-Instruct',
'Qwen/Qwen2.5-72B-Instruct',
'meta-llama/Llama-3.1-70B-Instruct',
'mistralai/Mixtral-8x7B-Instruct-v0.1',
# Mid-size Models
'meta-llama/Llama-3.1-8B-Instruct',
'google/gemma-2-9b-it',
'mistralai/Mistral-7B-v0.1',
'meta-llama/Llama-2-7b-chat-hf',
# Smaller Models
'meta-llama/Llama-3.2-3B-Instruct',
'meta-llama/Llama-3.2-1B-Instruct',
'Qwen/Qwen2.5-1.5B-Instruct',
'microsoft/Phi-3.5-mini-instruct',
'HuggingFaceTB/SmolLM2-1.7B-Instruct',
'google/gemma-2-2b-it',
# Base Models
'meta-llama/Llama-3.2-3B',
'meta-llama/Llama-3.2-1B',
'openai-community/gpt2'
],
value='HuggingFaceTB/SmolLM2-1.7B-Instruct', # Default to a powerful model
label="Select Hugging Face Model",
interactive=True
)
hf_interface = gr.load(
name=hf_model.value,
src="models", # Use direct model loading from HF
fill_height=True
)
def update_hf_model(new_model):
return gr.load(
name=new_model,
src="models",
fill_height=True
)
hf_model.change(
fn=update_hf_model,
inputs=[hf_model],
outputs=[hf_interface]
)
gr.Markdown("""
**Note:** These models are loaded directly from Hugging Face Hub. Some models may require authentication.
Models are organized by size:
- **Large Models**: 32B-72B parameters
- **Mid-size Models**: 7B-9B parameters
- **Smaller Models**: 1B-3B parameters
- **Base Models**: Original architectures
Visit [Hugging Face](https://huggingface.co/) to learn more about available models.
""")
with gr.Tab("Groq"):
with gr.Row():
groq_model = gr.Dropdown(
choices=[
'llama3-groq-8b-8192-tool-use-preview',
'llama3-groq-70b-8192-tool-use-preview',
'llama-3.2-1b-preview',
'llama-3.2-3b-preview',
'llama-3.2-11b-text-preview',
'llama-3.2-90b-text-preview',
'mixtral-8x7b-32768',
'gemma2-9b-it',
'gemma-7b-it'
],
value='llama3-groq-70b-8192-tool-use-preview', # Default to Groq's optimized model
label="Select Groq Model",
interactive=True
)
groq_interface = gr.load(
name=groq_model.value,
src=groq_gradio.registry,
fill_height=True
)
def update_groq_model(new_model):
return gr.load(
name=new_model,
src=groq_gradio.registry,
fill_height=True
)
groq_model.change(
fn=update_groq_model,
inputs=[groq_model],
outputs=[groq_interface]
)
gr.Markdown("""
**Note:** You need a Groq API key to use these models. Get one at [Groq Cloud](https://console.groq.com/).
""")
with gr.Tab("Hyperbolic"):
with gr.Row():
hyperbolic_model = gr.Dropdown(
choices=[
# # Vision Models (TODO)
# 'Qwen/Qwen2-VL-72B-Instruct', # 32K context
# 'mistralai/Pixtral-12B-2409', # 32K context
# 'Qwen/Qwen2-VL-7B-Instruct', # 32K context
# Large Language Models
'Qwen/Qwen2.5-Coder-32B-Instruct', # 131K context
'meta-llama/Llama-3.2-3B-Instruct', # 131K context
'meta-llama/Meta-Llama-3.1-8B-Instruct', # 131k context
'meta-llama/Meta-Llama-3.1-70B-Instruct', # 32K context
'meta-llama/Meta-Llama-3-70B-Instruct', # 8K context
'NousResearch/Hermes-3-Llama-3.1-70B', # 12K context
'Qwen/Qwen2.5-72B-Instruct', # 32K context
'deepseek-ai/DeepSeek-V2.5', # 8K context
'meta-llama/Meta-Llama-3.1-405B-Instruct', # 8K context
],
value='Qwen/Qwen2.5-Coder-32B-Instruct',
label="Select Hyperbolic Model",
interactive=True
)
hyperbolic_interface = gr.load(
name=hyperbolic_model.value,
src=hyperbolic_gradio.registry,
fill_height=True
)
def update_hyperbolic_model(new_model):
return gr.load(
name=new_model,
src=hyperbolic_gradio.registry,
fill_height=True
)
hyperbolic_model.change(
fn=update_hyperbolic_model,
inputs=[hyperbolic_model],
outputs=[hyperbolic_interface]
)
gr.Markdown("""
**Note:** This model is supported by Hyperbolic. Build your AI apps at [Hyperbolic](https://app.hyperbolic.xyz/).
""")
with gr.Tab("Qwen"):
with gr.Row():
qwen_model = gr.Dropdown(
choices=[
# Proprietary Qwen Models
'qwen-turbo-latest',
'qwen-turbo',
'qwen-plus',
'qwen-max',
# Open Source Qwen Models
'qwen1.5-110b-chat',
'qwen1.5-72b-chat',
'qwen1.5-32b-chat',
'qwen1.5-14b-chat',
'qwen1.5-7b-chat'
],
value='qwen-turbo-latest', # Default to the latest turbo model
label="Select Qwen Model",
interactive=True
)
qwen_interface = gr.load(
name=qwen_model.value,
src=dashscope_gradio.registry,
fill_height=True
)
def update_qwen_model(new_model):
return gr.load(
name=new_model,
src=dashscope_gradio.registry,
fill_height=True
)
qwen_model.change(
fn=update_qwen_model,
inputs=[qwen_model],
outputs=[qwen_interface]
)
gr.Markdown("""
**Note:** You need a DashScope API key to use these models. Get one at [DashScope](https://dashscope.aliyun.com/).
Models available in two categories:
- **Proprietary Models**:
- Qwen Turbo: Fast responses for general tasks
- Qwen Plus: Balanced performance and quality
- Qwen Max: Highest quality responses
- **Open Source Models**:
- Available in various sizes from 7B to 110B parameters
- Based on the Qwen 1.5 architecture
""")
with gr.Tab("Perplexity"):
with gr.Row():
perplexity_model = gr.Dropdown(
choices=[
# Sonar Models (Online)
'llama-3.1-sonar-small-128k-online', # 8B params
'llama-3.1-sonar-large-128k-online', # 70B params
'llama-3.1-sonar-huge-128k-online', # 405B params
# Sonar Models (Chat)
'llama-3.1-sonar-small-128k-chat', # 8B params
'llama-3.1-sonar-large-128k-chat', # 70B params
# Open Source Models
'llama-3.1-8b-instruct', # 8B params
'llama-3.1-70b-instruct' # 70B params
],
value='llama-3.1-sonar-large-128k-online', # Default to large online model
label="Select Perplexity Model",
interactive=True
)
perplexity_interface = gr.load(
name=perplexity_model.value,
src=perplexity_gradio.registry,
accept_token=True,
fill_height=True
)
def update_perplexity_model(new_model):
return gr.load(
name=new_model,
src=perplexity_gradio.registry,
accept_token=True,
fill_height=True
)
perplexity_model.change(
fn=update_perplexity_model,
inputs=[perplexity_model],
outputs=[perplexity_interface]
)
gr.Markdown("""
**Note:** Models are grouped into three categories:
- **Sonar Online Models**: Include search capabilities (beta access required)
- **Sonar Chat Models**: Standard chat models
- **Open Source Models**: Based on Hugging Face implementations
For access to Online LLMs features, please fill out the [beta access form](https://perplexity.typeform.com/apiaccessform?typeform-source=docs.perplexity.ai).
""")
with gr.Tab("DeepSeek-V2.5"):
gr.load(
name='deepseek-ai/DeepSeek-V2.5',
src=hyperbolic_gradio.registry,
fill_height=True
)
gr.Markdown("""
**Note:** This model is supported by Hyperbolic. Build your AI apps at [Hyperbolic](https://app.hyperbolic.xyz/).
""")
with gr.Tab("Mistral"):
with gr.Row():
mistral_model = gr.Dropdown(
choices=[
# Premier Models
'mistral-large-latest', # Top-tier reasoning model (128k)
'pixtral-large-latest', # Frontier-class multimodal model (128k)
'ministral-3b-latest', # Best edge model (128k)
'ministral-8b-latest', # High performance edge model (128k)
'mistral-small-latest', # Enterprise-grade small model (32k)
'codestral-latest', # Code-specialized model (32k)
'mistral-embed', # Semantic text representation (8k)
'mistral-moderation-latest', # Content moderation service (8k)
# Free Models
'pixtral-12b-2409', # Free 12B multimodal model (128k)
'open-mistral-nemo', # Multilingual model (128k)
'open-codestral-mamba' # Mamba-based coding model (256k)
],
value='pixtral-large-latest', # pixtral for vision
label="Select Mistral Model",
interactive=True
)
mistral_interface = gr.load(
name=mistral_model.value,
src=mistral_gradio.registry,
fill_height=True
)
def update_mistral_model(new_model):
return gr.load(
name=new_model,
src=mistral_gradio.registry,
fill_height=True
)
mistral_model.change(
fn=update_mistral_model,
inputs=[mistral_model],
outputs=[mistral_interface],
)
gr.Markdown("""
**Note:** You need a Mistral API key to use these models. Get one at [Mistral AI Platform](https://console.mistral.ai/).
Models are grouped into two categories:
- **Premier Models**: Require a paid API key
- **Free Models**: Available with free API keys
Each model has different context window sizes (from 8k to 256k tokens) and specialized capabilities.
""")
with gr.Tab("Fireworks"):
with gr.Row():
fireworks_model = gr.Dropdown(
choices=[
'f1-preview', # Latest F1 preview model
'f1-mini-preview', # Smaller, faster model
],
value='f1-preview', # Default to preview model
label="Select Fireworks Model",
interactive=True
)
fireworks_interface = gr.load(
name=fireworks_model.value,
src=fireworks_gradio.registry,
fill_height=True
)
def update_fireworks_model(new_model):
return gr.load(
name=new_model,
src=fireworks_gradio.registry,
fill_height=True
)
fireworks_model.change(
fn=update_fireworks_model,
inputs=[fireworks_model],
outputs=[fireworks_interface]
)
gr.Markdown("""
**Note:** You need a Fireworks AI API key to use these models. Get one at [Fireworks AI](https://app.fireworks.ai/).
""")
with gr.Tab("Cerebras"):
with gr.Row():
cerebras_model = gr.Dropdown(
choices=[
'llama3.1-8b',
'llama3.1-70b',
'llama3.1-405b'
],
value='llama3.1-70b', # Default to mid-size model
label="Select Cerebras Model",
interactive=True
)
cerebras_interface = gr.load(
name=cerebras_model.value,
src=cerebras_gradio.registry,
accept_token=True, # Added token acceptance
fill_height=True
)
def update_cerebras_model(new_model):
return gr.load(
name=new_model,
src=cerebras_gradio.registry,
accept_token=True, # Added token acceptance
fill_height=True
)
cerebras_model.change(
fn=update_cerebras_model,
inputs=[cerebras_model],
outputs=[cerebras_interface]
)
with gr.Tab("Together"):
with gr.Row():
together_model = gr.Dropdown(
choices=[
# Vision Models
'meta-llama/Llama-Vision-Free', # 131k context (Free)
'meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo', # 131k context
'meta-llama/Llama-3.2-90B-Vision-Instruct-Turbo', # 131k context
# Meta Llama 3.x Models
'meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo', # 131k context
'meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo', # 131k context
'meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo', # 130k context
'meta-llama/Meta-Llama-3-8B-Instruct-Turbo', # 8k context
'meta-llama/Meta-Llama-3-70B-Instruct-Turbo', # 8k context
'meta-llama/Llama-3.2-3B-Instruct-Turbo', # 131k context
'meta-llama/Meta-Llama-3-8B-Instruct-Lite', # 8k context, INT4
'meta-llama/Meta-Llama-3-70B-Instruct-Lite', # 8k context, INT4
'meta-llama/Llama-3-8b-chat-hf', # 8k context
'meta-llama/Llama-3-70b-chat-hf', # 8k context
# Other Large Models
'nvidia/Llama-3.1-Nemotron-70B-Instruct-HF', # 32k context
'Qwen/Qwen2.5-Coder-32B-Instruct', # 32k context
'microsoft/WizardLM-2-8x22B', # 65k context
'google/gemma-2-27b-it', # 8k context
'google/gemma-2-9b-it', # 8k context
'databricks/dbrx-instruct', # 32k context
# Mixtral Models
'mistralai/Mixtral-8x7B-Instruct-v0.1', # 32k context
'mistralai/Mixtral-8x22B-Instruct-v0.1', # 65k context
# Qwen Models
'Qwen/Qwen2.5-7B-Instruct-Turbo', # 32k context
'Qwen/Qwen2.5-72B-Instruct-Turbo', # 32k context
'Qwen/Qwen2-72B-Instruct', # 32k context
# Other Models
'deepseek-ai/deepseek-llm-67b-chat', # 4k context
'google/gemma-2b-it', # 8k context
'Gryphe/MythoMax-L2-13b', # 4k context
'meta-llama/Llama-2-13b-chat-hf', # 4k context
'mistralai/Mistral-7B-Instruct-v0.1', # 8k context
'mistralai/Mistral-7B-Instruct-v0.2', # 32k context
'mistralai/Mistral-7B-Instruct-v0.3', # 32k context
'NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO', # 32k context
'togethercomputer/StripedHyena-Nous-7B', # 32k context
'upstage/SOLAR-10.7B-Instruct-v1.0' # 4k context
],
value='meta-llama/Llama-3.2-11B-Vision-Instruct-Turbo', # Default to recommended vision model
label="Select Together Model",
interactive=True
)
together_interface = gr.load(
name=together_model.value,
src=together_gradio.registry,
multimodal=True,
fill_height=True
)
def update_together_model(new_model):
return gr.load(
name=new_model,
src=together_gradio.registry,
multimodal=True,
fill_height=True
)
together_model.change(
fn=update_together_model,
inputs=[together_model],
outputs=[together_interface]
)
gr.Markdown("""
**Note:** You need a Together AI API key to use these models. Get one at [Together AI](https://www.together.ai/).
""")
with gr.Tab("NVIDIA"):
with gr.Row():
nvidia_model = gr.Dropdown(
choices=[
# NVIDIA Models
'nvidia/llama3-chatqa-1.5-70b',
'nvidia/llama3-chatqa-1.5-8b',
'nvidia-nemotron-4-340b-instruct',
# Meta Models
'meta/llama-3.1-70b-instruct', # Added Llama 3.1 70B
'meta/codellama-70b',
'meta/llama2-70b',
'meta/llama3-8b',
'meta/llama3-70b',
# Mistral Models
'mistralai/codestral-22b-instruct-v0.1',
'mistralai/mathstral-7b-v0.1',
'mistralai/mistral-large-2-instruct',
'mistralai/mistral-7b-instruct',
'mistralai/mistral-7b-instruct-v0.3',
'mistralai/mixtral-8x7b-instruct',
'mistralai/mixtral-8x22b-instruct',
'mistralai/mistral-large',
# Google Models
'google/gemma-2b',
'google/gemma-7b',
'google/gemma-2-2b-it',
'google/gemma-2-9b-it',
'google/gemma-2-27b-it',
'google/codegemma-1.1-7b',
'google/codegemma-7b',
'google/recurrentgemma-2b',
'google/shieldgemma-9b',
# Microsoft Phi-3 Models
'microsoft/phi-3-medium-128k-instruct',
'microsoft/phi-3-medium-4k-instruct',
'microsoft/phi-3-mini-128k-instruct',
'microsoft/phi-3-mini-4k-instruct',
'microsoft/phi-3-small-128k-instruct',
'microsoft/phi-3-small-8k-instruct',
# Other Models
'qwen/qwen2-7b-instruct',
'databricks/dbrx-instruct',
'deepseek-ai/deepseek-coder-6.7b-instruct',
'upstage/solar-10.7b-instruct',
'snowflake/arctic'
],
value='meta/llama-3.1-70b-instruct', # Changed default to Llama 3.1 70B
label="Select NVIDIA Model",
interactive=True
)
nvidia_interface = gr.load(
name=nvidia_model.value,
src=nvidia_gradio.registry,
accept_token=True,
fill_height=True
)
def update_nvidia_model(new_model):
return gr.load(
name=new_model,
src=nvidia_gradio.registry,
accept_token=True,
fill_height=True
)
nvidia_model.change(
fn=update_nvidia_model,
inputs=[nvidia_model],
outputs=[nvidia_interface]
)
gr.Markdown("""
**Note:** You need an NVIDIA AI Foundation API key to use these models. Get one at [NVIDIA AI Foundation](https://www.nvidia.com/en-us/ai-data-science/foundation-models/).
Models are organized by provider:
- **NVIDIA**: Native models including Llama3-ChatQA and Nemotron
- **Meta**: Llama family models
- **Mistral**: Various Mistral and Mixtral models
- **Google**: Gemma family models
- **Microsoft**: Phi-3 series
- And other providers including Qwen, Databricks, DeepSeek, etc.
""")
demo.launch(ssr_mode=False)
