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"""ForgeKit — Forge your perfect AI model, no code required.
Main Gradio application with 5 tabs:
1. Merge Builder — Visual merge configuration + notebook generation
2. Model Explorer — Search and discover HF models
3. GGUF Quantizer — Generate quantization notebooks
4. Deploy — Generate deployment files for HF Spaces
5. Leaderboard — Community merge rankings
"""
import gradio as gr
import json
import tempfile
import os
from forgekit.model_info import fetch_model_info, search_models
from forgekit.compatibility import check_compatibility, quick_check
from forgekit.config_generator import (
MergeConfig, generate_yaml, generate_from_preset,
MERGE_METHODS, PRESETS,
)
from forgekit.notebook_generator import generate_merge_notebook, save_notebook
from forgekit.ai_advisor import merge_advisor, model_describer, config_explainer
from forgekit.kaggle_runner import push_and_run_kernel, check_kernel_status, generate_kaggle_notebook
# ===== THEME =====
theme = gr.themes.Base(
primary_hue=gr.themes.colors.amber,
secondary_hue=gr.themes.colors.purple,
neutral_hue=gr.themes.colors.gray,
font=gr.themes.GoogleFont("Inter"),
font_mono=gr.themes.GoogleFont("JetBrains Mono"),
).set(
body_background_fill="#0a0a0f",
body_background_fill_dark="#0a0a0f",
body_text_color="#e5e5e5",
body_text_color_dark="#e5e5e5",
block_background_fill="#111118",
block_background_fill_dark="#111118",
block_border_color="#1f1f2e",
block_border_color_dark="#1f1f2e",
block_label_text_color="#9ca3af",
block_label_text_color_dark="#9ca3af",
block_title_text_color="#e5e5e5",
block_title_text_color_dark="#e5e5e5",
input_background_fill="#16161f",
input_background_fill_dark="#16161f",
input_border_color="#2a2a3a",
input_border_color_dark="#2a2a3a",
button_primary_background_fill="linear-gradient(to right, #f59e0b, #f97316)",
button_primary_background_fill_dark="linear-gradient(to right, #f59e0b, #f97316)",
button_primary_text_color="#ffffff",
button_primary_text_color_dark="#ffffff",
button_secondary_background_fill="#1f1f2e",
button_secondary_background_fill_dark="#1f1f2e",
button_secondary_text_color="#e5e5e5",
button_secondary_text_color_dark="#e5e5e5",
)
CSS = """
.forgekit-header { text-align: center; padding: 1.5rem 0 1rem; }
.forgekit-header h1 { font-size: 2.5rem; font-weight: 800; margin: 0;
background: linear-gradient(135deg, #a855f7, #ec4899, #f59e0b);
-webkit-background-clip: text; -webkit-text-fill-color: transparent; }
.forgekit-header p { color: #9ca3af; font-size: 1rem; margin-top: 0.25rem; }
.status-ok { color: #4ade80; font-weight: 600; }
.status-warn { color: #fbbf24; font-weight: 600; }
.status-err { color: #f87171; font-weight: 600; }
.method-card { border: 1px solid #2a2a3a; border-radius: 12px; padding: 1rem; margin: 0.25rem 0; }
footer { display: none !important; }
"""
# ===== CALLBACKS =====
def check_models(models_text: str, token: str) -> tuple[str, str]:
"""Check model compatibility and return report + quick status."""
models = [m.strip() for m in models_text.strip().split("\n") if m.strip()]
if len(models) < 2:
return "⚠️ Add at least 2 models (one per line)", ""
tok = token.strip() if token else None
report = check_compatibility(models, token=tok)
quick = quick_check(models, token=tok)
return report.to_markdown(), quick
def generate_config(
models_text: str, method: str, base_model: str,
weights_text: str, densities_text: str,
tokenizer_src: str, dtype: str,
slerp_t: float, int8_mask: bool, normalize: bool,
) -> str:
"""Generate YAML config from UI inputs."""
models = [m.strip() for m in models_text.strip().split("\n") if m.strip()]
if not models:
return "# Add models first"
# Parse weights
weights = []
if weights_text.strip():
try:
weights = [float(w.strip()) for w in weights_text.split(",")]
except ValueError:
return "# Invalid weights — use comma-separated numbers"
densities = []
if densities_text.strip():
try:
densities = [float(d.strip()) for d in densities_text.split(",")]
except ValueError:
return "# Invalid densities — use comma-separated numbers"
config = MergeConfig(
method=method,
models=models,
base_model=base_model.strip(),
weights=weights,
densities=densities,
tokenizer_source=tokenizer_src.strip(),
dtype=dtype,
slerp_t=slerp_t,
int8_mask=int8_mask,
normalize=normalize,
)
return generate_yaml(config)
def apply_preset(preset_name: str, models_text: str) -> tuple[str, str]:
"""Apply a preset and return weights + densities strings."""
models = [m.strip() for m in models_text.strip().split("\n") if m.strip()]
if not models:
return "", ""
preset = PRESETS.get(preset_name)
if not preset:
return "", ""
weights, densities = preset.apply(models)
return ", ".join(str(w) for w in weights), ", ".join(str(d) for d in densities)
def generate_notebook_file(
models_text: str, method: str, base_model: str,
weights_text: str, densities_text: str,
tokenizer_src: str, dtype: str,
slerp_t: float, int8_mask: bool, normalize: bool,
output_name: str, hf_user: str,
inc_quantize: bool, inc_deploy: bool,
quant_types_text: str,
) -> str | None:
"""Generate and save a Colab notebook, return file path."""
models = [m.strip() for m in models_text.strip().split("\n") if m.strip()]
if not models:
return None
weights = []
if weights_text.strip():
try:
weights = [float(w.strip()) for w in weights_text.split(",")]
except ValueError:
pass
densities = []
if densities_text.strip():
try:
densities = [float(d.strip()) for d in densities_text.split(",")]
except ValueError:
pass
quant_types = [q.strip() for q in quant_types_text.split(",") if q.strip()]
if not quant_types:
quant_types = ["Q5_K_M", "Q4_K_M"]
config = MergeConfig(
method=method,
models=models,
base_model=base_model.strip(),
weights=weights,
densities=densities,
tokenizer_source=tokenizer_src.strip(),
dtype=dtype,
slerp_t=slerp_t,
int8_mask=int8_mask,
normalize=normalize,
)
name = output_name.strip() or "ForgeKit-Merged-Model"
user = hf_user.strip()
nb = generate_merge_notebook(
config,
output_model_name=name,
hf_username=user,
include_quantize=inc_quantize,
include_deploy=inc_deploy,
quant_types=quant_types,
)
path = os.path.join(tempfile.gettempdir(), f"{name}_merge.ipynb")
save_notebook(nb, path)
return path
def search_hf_models(query: str, arch_filter: str, sort_by: str) -> str:
"""Search HF Hub and return formatted results."""
if not query.strip():
return "Enter a search query"
results = search_models(
query=query.strip(),
architecture=arch_filter if arch_filter != "Any" else "",
limit=15,
sort=sort_by.lower(),
)
if not results:
return "No models found"
lines = ["| Model | Architecture | Downloads |", "|-------|-------------|-----------|"]
for r in results:
mid = r.get("model_id", "")
mtype = r.get("model_type", "—")
dl = r.get("downloads", 0)
dl_str = f"{dl:,}" if dl else "—"
lines.append(f"| `{mid}` | {mtype} | {dl_str} |")
return "\n".join(lines)
def fetch_model_details(model_id: str) -> str:
"""Fetch and display detailed model info."""
if not model_id.strip():
return "Enter a model ID"
info = fetch_model_info(model_id.strip())
if info.error:
return f"❌ {info.error}"
return f"""### {info.model_id}
| Property | Value |
|----------|-------|
| **Architecture** | `{info.model_type}` |
| **Hidden Size** | {info.hidden_size} |
| **Layers** | {info.num_hidden_layers} |
| **Vocab Size** | {info.vocab_size:,} |
| **Intermediate** | {info.intermediate_size} |
| **Attention Heads** | {info.num_attention_heads} |
| **KV Heads** | {info.num_key_value_heads} |
| **Max Position** | {info.max_position_embeddings:,} |
| **dtype** | {info.torch_dtype} |
| **Downloads** | {info.downloads:,} |
| **Likes** | {info.likes} |
| **Params (est.)** | {info.param_estimate} |
| **RAM for merge** | {info.ram_estimate_gb} GB |
| **Gated** | {'Yes' if info.gated else 'No'} |
| **trust_remote_code** | {'Required' if info.trust_remote_code else 'No'} |"""
def suggest_base(models_text: str, token: str) -> tuple[str, str]:
"""Auto-suggest base model and tokenizer from compatibility check."""
models = [m.strip() for m in models_text.strip().split("\n") if m.strip()]
if len(models) < 2:
return "", ""
tok = token.strip() if token else None
report = check_compatibility(models, token=tok)
return report.suggested_base, report.suggested_tokenizer
# ===== LEADERBOARD DATA =====
# Seeded with your existing merges
LEADERBOARD = [
{
"name": "Qwen2.5CMR-7B", "author": "AIencoder",
"method": "DARE-TIES", "base": "Qwen2.5-7B-Instruct",
"models": "Coder-7B + Math-7B", "likes": 0,
"link": "https://huggingface.co/AIencoder/Qwen2.5CMR",
},
{
"name": "Logic-Coder-7B", "author": "AIencoder",
"method": "DARE-TIES", "base": "Mistral-7B",
"models": "OpenHermes + CodeInstruct", "likes": 1,
"link": "https://huggingface.co/AIencoder/Logic-Coder-7B",
},
{
"name": "HermesMath-7B-TIES", "author": "AIencoder",
"method": "TIES", "base": "Mistral-7B",
"models": "Hermes + MetaMath", "likes": 1,
"link": "https://huggingface.co/AIencoder/HermesMath-7B-TIES",
},
{
"name": "Hermes-2-Pro-GodCoder", "author": "AIencoder",
"method": "DARE-TIES", "base": "Mistral-7B",
"models": "Hermes-2-Pro + CodeModels", "likes": 1,
"link": "https://huggingface.co/AIencoder/Hermes-2-Pro-Mistral-7B-GodCoder",
},
]
def get_leaderboard() -> str:
"""Return leaderboard as markdown table."""
lines = [
"| # | Model | Author | Method | Source Models | Likes |",
"|---|-------|--------|--------|---------------|-------|",
]
sorted_lb = sorted(LEADERBOARD, key=lambda x: -x["likes"])
for i, entry in enumerate(sorted_lb, 1):
name = f"[{entry['name']}]({entry['link']})"
lines.append(
f"| {i} | {name} | {entry['author']} | {entry['method']} | "
f"{entry['models']} | {entry['likes']} |"
)
return "\n".join(lines)
# ============================================================
# GRADIO APP
# ============================================================
with gr.Blocks(theme=theme, css=CSS, title="ForgeKit — Model Merging Platform") as demo:
# ===== HEADER =====
gr.HTML("""
<div class="forgekit-header">
<h1>🔥 ForgeKit</h1>
<p>Forge your perfect AI model — no code required</p>
</div>
""")
with gr.Tabs():
# =====================================================
# TAB 1: MERGE BUILDER
# =====================================================
with gr.Tab("⚒️ Merge Builder", id="builder"):
gr.Markdown("### Build your merge configuration and generate a ready-to-run Colab notebook")
with gr.Row():
# LEFT COLUMN: Inputs
with gr.Column(scale=3):
models_input = gr.Textbox(
label="Models to Merge (one per line)",
placeholder="Qwen/Qwen2.5-Coder-7B-Instruct\nQwen/Qwen2.5-Math-7B-Instruct",
lines=5,
)
hf_token = gr.Textbox(
label="HF Token (optional — for gated models)",
type="password",
placeholder="hf_...",
)
with gr.Row():
check_btn = gr.Button("🔍 Check Compatibility", variant="secondary")
suggest_btn = gr.Button("💡 Auto-Suggest Base", variant="secondary")
compat_status = gr.Textbox(label="Quick Status", interactive=False, max_lines=2)
compat_report = gr.Markdown(label="Compatibility Report")
# RIGHT COLUMN: Configuration
with gr.Column(scale=3):
method_dd = gr.Dropdown(
choices=list(MERGE_METHODS.keys()),
value="dare_ties",
label="Merge Method",
)
method_info_md = gr.Markdown(
value=f"**DARE-TIES** — {MERGE_METHODS['dare_ties']['description']}"
)
base_model = gr.Textbox(
label="Base Model",
placeholder="Qwen/Qwen2.5-7B-Instruct",
)
tokenizer_src = gr.Textbox(
label="Tokenizer Source",
placeholder="Same as base model (leave blank to auto-fill)",
)
with gr.Row():
weights_input = gr.Textbox(label="Weights (comma-separated)", placeholder="0.5, 0.5")
densities_input = gr.Textbox(label="Densities (comma-separated)", placeholder="0.7, 0.6")
with gr.Row():
preset_dd = gr.Dropdown(
choices=list(PRESETS.keys()),
label="Apply Preset",
scale=2,
)
preset_btn = gr.Button("Apply", variant="secondary", scale=1)
with gr.Row():
dtype_dd = gr.Dropdown(choices=["bfloat16", "float16", "float32"], value="bfloat16", label="dtype")
slerp_t = gr.Slider(0, 1, value=0.5, step=0.05, label="SLERP t", visible=False)
with gr.Row():
int8_mask = gr.Checkbox(label="int8_mask", value=True)
normalize_cb = gr.Checkbox(label="normalize", value=True)
gr.Markdown("---")
gr.Markdown("### Output")
with gr.Row():
with gr.Column(scale=3):
yaml_output = gr.Code(label="Generated YAML Config", language="yaml", lines=15)
gen_yaml_btn = gr.Button("📋 Generate YAML", variant="primary", size="lg")
with gr.Column(scale=3):
gr.Markdown("#### Notebook Settings")
output_name = gr.Textbox(label="Model Name", placeholder="My-Merged-7B")
hf_username = gr.Textbox(label="HF Username", placeholder="AIencoder")
with gr.Row():
inc_quant = gr.Checkbox(label="Include GGUF Quantization", value=True)
inc_deploy = gr.Checkbox(label="Include HF Deployment", value=True)
quant_types = gr.Textbox(label="Quant Types", value="Q5_K_M, Q4_K_M")
gen_nb_btn = gr.Button("🚀 Generate Colab Notebook", variant="primary", size="lg")
nb_file = gr.File(label="Download Notebook")
# === EVENTS ===
check_btn.click(
check_models, [models_input, hf_token], [compat_report, compat_status]
)
suggest_btn.click(
suggest_base, [models_input, hf_token], [base_model, tokenizer_src]
)
preset_btn.click(
apply_preset, [preset_dd, models_input], [weights_input, densities_input]
)
gen_yaml_btn.click(
generate_config,
[models_input, method_dd, base_model, weights_input, densities_input,
tokenizer_src, dtype_dd, slerp_t, int8_mask, normalize_cb],
yaml_output,
)
gen_nb_btn.click(
generate_notebook_file,
[models_input, method_dd, base_model, weights_input, densities_input,
tokenizer_src, dtype_dd, slerp_t, int8_mask, normalize_cb,
output_name, hf_username, inc_quant, inc_deploy, quant_types],
nb_file,
)
# Method change: show/hide SLERP slider + update description
def on_method_change(m):
info = MERGE_METHODS.get(m, {})
desc = f"**{info.get('name', m)}** — {info.get('description', '')}"
show_slerp = m == "slerp"
return desc, gr.update(visible=show_slerp)
method_dd.change(on_method_change, method_dd, [method_info_md, slerp_t])
# =====================================================
# TAB 2: MODEL EXPLORER
# =====================================================
with gr.Tab("🔍 Model Explorer", id="explorer"):
gr.Markdown("### Search and discover models on HuggingFace Hub")
with gr.Row():
search_query = gr.Textbox(label="Search", placeholder="qwen coder instruct", scale=3)
arch_filter = gr.Dropdown(
choices=["Any", "llama", "qwen2", "mistral", "gemma2", "phi3", "starcoder2"],
value="Any", label="Architecture", scale=1,
)
sort_dd = gr.Dropdown(choices=["Downloads", "Likes", "Modified"], value="Downloads", label="Sort", scale=1)
search_btn = gr.Button("🔍 Search", variant="primary", scale=1)
search_results = gr.Markdown(label="Results")
gr.Markdown("---")
gr.Markdown("### Model Details")
with gr.Row():
detail_input = gr.Textbox(label="Model ID", placeholder="Qwen/Qwen2.5-Coder-7B-Instruct", scale=3)
detail_btn = gr.Button("📋 Fetch Details", variant="secondary", scale=1)
detail_output = gr.Markdown()
search_btn.click(search_hf_models, [search_query, arch_filter, sort_dd], search_results)
detail_btn.click(fetch_model_details, detail_input, detail_output)
# =====================================================
# TAB 3: GGUF QUANTIZER
# =====================================================
with gr.Tab("📦 GGUF Quantizer", id="quantizer"):
gr.Markdown("""### Generate a quantization notebook for any HF model
Convert any HuggingFace model to GGUF format for use with llama.cpp, Ollama, LM Studio, etc.""")
q_model = gr.Textbox(label="Model ID", placeholder="AIencoder/Qwen2.5CMR-7B")
q_username = gr.Textbox(label="Your HF Username", placeholder="AIencoder")
gr.Markdown("#### Quantization Levels")
gr.Markdown("""
| Type | Size (7B) | Quality | Best For |
|------|----------|---------|----------|
| Q8_0 | ~7.5 GB | Best | Maximum quality |
| Q6_K | ~5.5 GB | Great | Good balance |
| **Q5_K_M** | **~5 GB** | **Good** | **Recommended** |
| Q4_K_M | ~4 GB | Decent | Memory-constrained |
| IQ4_XS | ~3.5 GB | Fair | Extreme compression |
""")
q_types = gr.Textbox(label="Quant Types (comma-separated)", value="Q8_0, Q5_K_M, Q4_K_M")
q_btn = gr.Button("📦 Generate Quantization Notebook", variant="primary", size="lg")
q_file = gr.File(label="Download Notebook")
def gen_quant_notebook(model_id, username, qtypes_text):
if not model_id.strip():
return None
qtypes = [q.strip() for q in qtypes_text.split(",") if q.strip()]
name = model_id.strip().split("/")[-1]
config = MergeConfig(method="linear", models=[model_id.strip()])
nb = generate_merge_notebook(
config,
output_model_name=name,
hf_username=username.strip(),
include_quantize=True,
include_deploy=False,
quant_types=qtypes,
)
# Remove merge cells, keep only setup + quantize
path = os.path.join(tempfile.gettempdir(), f"{name}_quantize.ipynb")
save_notebook(nb, path)
return path
q_btn.click(gen_quant_notebook, [q_model, q_username, q_types], q_file)
# =====================================================
# TAB 4: DEPLOY
# =====================================================
with gr.Tab("🚀 Deploy", id="deploy"):
gr.Markdown("""### Deploy your merged model to a HuggingFace Space
After merging and (optionally) quantizing, deploy a chat interface for your model.""")
d_model = gr.Textbox(label="Model Repo ID", placeholder="AIencoder/Qwen2.5CMR-7B")
d_type = gr.Dropdown(
choices=["Gradio Chat (transformers)", "Docker + llama.cpp (GGUF)"],
value="Gradio Chat (transformers)", label="Deployment Type",
)
d_btn = gr.Button("📋 Generate Deployment Files", variant="primary")
d_output = gr.Code(label="app.py", language="python", lines=20)
d_readme = gr.Code(label="README.md (Space metadata)", language="markdown", lines=8)
def gen_deploy(model_id, deploy_type):
mid = model_id.strip()
if not mid:
return "# Enter a model ID first", ""
if "Gradio" in deploy_type:
app = f'''import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, TextIteratorStreamer
import torch
from threading import Thread
MODEL_ID = "{mid}"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True
)
def chat(message, history):
messages = []
for h in history:
messages.append({{"role": "user", "content": h[0]}})
if h[1]:
messages.append({{"role": "assistant", "content": h[1]}})
messages.append({{"role": "user", "content": message}})
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
thread = Thread(target=model.generate, kwargs={{
**inputs, "max_new_tokens": 512, "streamer": streamer,
"do_sample": True, "temperature": 0.7,
}})
thread.start()
response = ""
for token in streamer:
response += token
yield response
demo = gr.ChatInterface(chat, title="{mid.split('/')[-1]}", description="Merged with ForgeKit")
demo.launch()'''
readme = f"""---
title: {mid.split('/')[-1]} Chat
emoji: 🔥
colorFrom: amber
colorTo: orange
sdk: gradio
sdk_version: 5.12.0
app_file: app.py
pinned: false
license: apache-2.0
---"""
else:
app = f'''# Docker deployment with llama.cpp
# Dockerfile for serving GGUF models
FROM ghcr.io/ggerganov/llama.cpp:server
# Download the GGUF model
ADD https://huggingface.co/{mid}/resolve/main/*Q5_K_M*.gguf /models/model.gguf
EXPOSE 7860
CMD ["/llama-server", \\
"--model", "/models/model.gguf", \\
"--host", "0.0.0.0", \\
"--port", "7860", \\
"--ctx-size", "4096", \\
"--n-gpu-layers", "99"]'''
readme = f"""---
title: {mid.split('/')[-1]}
emoji: 🔥
colorFrom: amber
colorTo: orange
sdk: docker
pinned: false
license: apache-2.0
---"""
return app, readme
d_btn.click(gen_deploy, [d_model, d_type], [d_output, d_readme])
# =====================================================
# TAB 5: AI ADVISOR
# =====================================================
with gr.Tab("🤖 AI Advisor", id="ai"):
gr.Markdown("""### AI-Powered Merge Intelligence
Get smart recommendations, capability predictions, and plain-English explanations — powered by **Llama 3.3 70B** on Groq (free, blazing fast).""")
groq_key = gr.Textbox(
label="Groq API Key (free at console.groq.com)",
type="password",
placeholder="gsk_... (free, no credit card needed)",
)
with gr.Tabs():
# --- Merge Advisor ---
with gr.Tab("💡 Merge Advisor"):
gr.Markdown("**Tell the AI what models you want to merge and it'll recommend the best strategy.**")
ai_models = gr.Textbox(
label="Models (one per line)",
placeholder="Qwen/Qwen2.5-Coder-7B-Instruct\nQwen/Qwen2.5-Math-7B-Instruct",
lines=4,
)
ai_goal = gr.Textbox(
label="What do you want the merged model to do?",
placeholder="I want a model that's great at both coding and math reasoning",
)
ai_advise_btn = gr.Button("💡 Get Recommendation", variant="primary")
ai_advise_out = gr.Markdown()
ai_advise_btn.click(
merge_advisor, [ai_models, ai_goal, groq_key], ai_advise_out
)
# --- Model Describer ---
with gr.Tab("🔮 Capability Predictor"):
gr.Markdown("**Predict what your merged model will be good (and bad) at.**")
desc_models = gr.Textbox(
label="Models (one per line)",
placeholder="Qwen/Qwen2.5-Coder-7B-Instruct\nQwen/Qwen2.5-Math-7B-Instruct",
lines=4,
)
desc_method = gr.Textbox(label="Merge Method", placeholder="dare_ties")
desc_weights = gr.Textbox(label="Weights", placeholder="0.5, 0.5")
desc_btn = gr.Button("🔮 Predict Capabilities", variant="primary")
desc_out = gr.Markdown()
desc_btn.click(
model_describer, [desc_models, desc_method, desc_weights, groq_key], desc_out
)
# --- Config Explainer ---
with gr.Tab("📖 Config Explainer"):
gr.Markdown("**Paste any mergekit YAML config and get a plain-English explanation.**")
explain_yaml = gr.Code(
label="Paste YAML Config",
language="yaml",
lines=12,
value="""merge_method: dare_ties
base_model: Qwen/Qwen2.5-7B-Instruct
models:
- model: Qwen/Qwen2.5-Coder-7B-Instruct
parameters:
weight: 0.5
density: 0.7
- model: Qwen/Qwen2.5-Math-7B-Instruct
parameters:
weight: 0.5
density: 0.6
parameters:
int8_mask: true
normalize: true
dtype: bfloat16""",
)
explain_btn = gr.Button("📖 Explain This Config", variant="primary")
explain_out = gr.Markdown()
explain_btn.click(
config_explainer, [explain_yaml, groq_key], explain_out
)
# =====================================================
# TAB 6: KAGGLE RUNNER
# =====================================================
with gr.Tab("🚀 Run on Kaggle", id="kaggle"):
gr.Markdown("""### Run Your Merge on Kaggle's Free GPU
Push your merge notebook directly to Kaggle and run it on a free T4 GPU — no local setup needed.
**You need:** A [Kaggle account](https://www.kaggle.com) with an API token. Go to *Settings > API > Create New Token*.""")
with gr.Row():
with gr.Column():
kg_username = gr.Textbox(label="Kaggle Username", placeholder="your_kaggle_username")
kg_key = gr.Textbox(label="Kaggle API Key", type="password", placeholder="From kaggle.json")
with gr.Column():
kg_hf_note = gr.Markdown("""**Important:** Add your HF token as a Kaggle Secret:
1. Go to your kernel's **Settings** tab
2. Under **Secrets**, add `HF_TOKEN` with your HuggingFace token
3. This lets the kernel download gated models and upload results""")
gr.Markdown("---")
gr.Markdown("#### Configure Merge (or use settings from Merge Builder tab)")
with gr.Row():
kg_models = gr.Textbox(
label="Models (one per line)", lines=4,
placeholder="Qwen/Qwen2.5-Coder-7B-Instruct\nQwen/Qwen2.5-Math-7B-Instruct",
)
with gr.Column():
kg_method = gr.Dropdown(choices=list(MERGE_METHODS.keys()), value="dare_ties", label="Method")
kg_base = gr.Textbox(label="Base Model", placeholder="Qwen/Qwen2.5-7B-Instruct")
kg_weights = gr.Textbox(label="Weights", placeholder="0.5, 0.5")
kg_densities = gr.Textbox(label="Densities", placeholder="0.7, 0.6")
with gr.Row():
kg_output_name = gr.Textbox(label="Output Model Name", placeholder="My-Merged-7B", value="ForgeKit-Merge")
kg_hf_user = gr.Textbox(label="HF Username (for upload)", placeholder="AIencoder")
kg_run_btn = gr.Button("🚀 Push & Run on Kaggle", variant="primary", size="lg")
kg_status = gr.Markdown()
def run_on_kaggle(
username, key, models_text, method, base, weights_text, densities_text,
output_name, hf_user,
):
# Build config
models = [m.strip() for m in models_text.strip().split("\n") if m.strip()]
if len(models) < 2:
return "Add at least 2 models."
weights = []
if weights_text.strip():
try:
weights = [float(w.strip()) for w in weights_text.split(",")]
except ValueError:
return "Invalid weights."
densities = []
if densities_text.strip():
try:
densities = [float(d.strip()) for d in densities_text.split(",")]
except ValueError:
return "Invalid densities."
config = MergeConfig(
method=method,
models=models,
base_model=base.strip(),
weights=weights,
densities=densities,
)
name = output_name.strip() or "ForgeKit-Merge"
# Generate notebook
nb = generate_merge_notebook(
config,
output_model_name=name,
hf_username=hf_user.strip(),
include_quantize=True,
include_deploy=False,
quant_types=["Q5_K_M", "Q4_K_M"],
)
# Adapt for Kaggle
kaggle_nb_json = generate_kaggle_notebook(nb)
# Push to Kaggle
result = push_and_run_kernel(
notebook_json=kaggle_nb_json,
kernel_title=f"ForgeKit-{name}",
kaggle_username=username.strip(),
kaggle_key=key.strip(),
enable_gpu=True,
enable_internet=True,
)
if result["success"]:
return result["message"]
else:
return result["error"]
kg_run_btn.click(
run_on_kaggle,
[kg_username, kg_key, kg_models, kg_method, kg_base, kg_weights, kg_densities,
kg_output_name, kg_hf_user],
kg_status,
)
gr.Markdown("---")
gr.Markdown("#### Check Kernel Status")
with gr.Row():
kg_check_slug = gr.Textbox(label="Kernel Slug", placeholder="username/forgekit-my-merged-7b")
kg_check_btn = gr.Button("🔍 Check Status", variant="secondary")
kg_check_out = gr.Markdown()
def check_status(slug, username, key):
if not slug.strip():
return "Enter a kernel slug (username/kernel-name)"
result = check_kernel_status(slug.strip(), username.strip(), key.strip())
if result["success"]:
msg = result["display"]
if result.get("failure_message"):
msg += f"\n\nError: {result['failure_message']}"
return msg
return result["error"]
kg_check_btn.click(check_status, [kg_check_slug, kg_username, kg_key], kg_check_out)
# =====================================================
# TAB 7: LEADERBOARD
# =====================================================
with gr.Tab("🏆 Leaderboard", id="leaderboard"):
gr.Markdown("""### Community Merge Leaderboard
See what others have built with ForgeKit. Submit your own merge to get featured!""")
lb_md = gr.Markdown(value=get_leaderboard())
lb_refresh = gr.Button("🔄 Refresh", variant="secondary")
lb_refresh.click(lambda: get_leaderboard(), outputs=lb_md)
gr.Markdown("---")
gr.Markdown("### Submit Your Merge")
with gr.Row():
sub_name = gr.Textbox(label="Model Name", placeholder="My-Awesome-Merge-7B")
sub_author = gr.Textbox(label="Author", placeholder="Your HF username")
sub_method = gr.Textbox(label="Merge Method", placeholder="DARE-TIES")
with gr.Row():
sub_models = gr.Textbox(label="Source Models (short)", placeholder="Coder-7B + Math-7B")
sub_link = gr.Textbox(label="HF Model Link", placeholder="https://huggingface.co/...")
sub_btn = gr.Button("📤 Submit", variant="primary")
sub_status = gr.Markdown()
def submit_merge(name, author, method, models, link):
if not all([name, author, method, models, link]):
return "⚠️ Please fill in all fields"
LEADERBOARD.append({
"name": name, "author": author, "method": method,
"base": "", "models": models, "likes": 0, "link": link,
})
return f"✅ **{name}** submitted! It will appear on the leaderboard."
sub_btn.click(submit_merge, [sub_name, sub_author, sub_method, sub_models, sub_link], sub_status)
# ===== FOOTER =====
gr.Markdown("""
---
<center>
**ForgeKit** v0.1.0 — Built by [AIencoder](https://huggingface.co/AIencoder) | [Portfolio](https://aiencoder-portfolio.static.hf.space) | [GitHub](https://github.com/Ary5272)
</center>
""")
if __name__ == "__main__":
demo.launch(server_name="0.0.0.0", server_port=7860) |