kadalicious22/snapgate-surge

Snapgate Surge

Surge is a 7B model from Snapgate AI, purpose-built to help developers, creators, and everyday users with coding, website building, and a wide range of daily tasks in both Indonesian and English.

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✨ Key Capabilities

Feature	Description
💻 Coding Assistant	PHP, JavaScript, Python, HTML/CSS, and 40+ programming languages
🌐 Web Development	Building, debugging, and optimizing websites
💬 Bilingual Chat	Indonesian and English
📝 Content Writing	Articles, documentation, and creative content
🔍 Code Review	Code analysis and optimization

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📊 Model Specifications

Specification	Details
Base Model	Qwen2.5 7B Instruct
Base Model Source	unsloth/Qwen2.5-7B-Instruct
Type	Causal Language Model
Parameters	7 Billion
Context Length	2048 tokens
Languages	Indonesian 🇮🇩, English 🇺🇸
Training Method	Supervised Fine-tuning (SFT) + LoRA
LoRA Rank	16
LoRA Alpha	32
LoRA Target Modules	q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Quantization	4-bit (NF4)
Training Framework	Unsloth + TRL
Batch Size	2 (per device)
Gradient Accumulation	4 steps (effective batch size: 8)
Learning Rate	2e-4
LR Scheduler	Linear
Optimizer	AdamW 8-bit
Training Steps	1,000 steps
Warmup Steps	10
Final Loss	~0.019
Use Case	Chat, Coding, Web Development
License	Apache 2.0

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📈 Training Progress

Step	Training Loss
50	0.031265
100	0.026168
150	0.024656
200	0.023187
250	0.021635
300	0.020565
350	0.021403
400	0.020508
450	0.021008
500	0.020904
550	0.020973
600	0.020271
650	0.020277
700	0.019679
750	0.019985
800	0.019746
850	0.019760
900	0.019363
950	0.019226
1000	0.019374

Loss decreased from 0.031 to 0.019 — converging well over 1,000 steps.

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🚀 Usage

Python — Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id  = "snapgate-ai/snapgate-surge"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model     = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype = torch.float16,
    device_map  = "auto"
)

SYSTEM_PROMPT = """You are Surge, an AI assistant from Snapgate AI (snapgate.tech).
You help users with coding, web development, and general questions
in both Indonesian and English."""

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user",   "content": "Who are you?"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize              = True,
    add_generation_prompt = True,
    return_tensors        = "pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens = 512,
    temperature    = 0.7,
    top_p          = 0.9,
    do_sample      = True,
)

response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True)
print(response)

---

REST API — PHP

<?php
class SnapgateSurge {
    private string $apiUrl;

    public function __construct(string $apiUrl = 'http://your-server:8000') {
        $this->apiUrl = $apiUrl;
    }

    public function chat(string $message): string {
        $payload = json_encode([
            'messages'    => [['role' => 'user', 'content' => $message]],
            'max_tokens'  => 512,
            'temperature' => 0.7,
        ]);

        $ch = curl_init($this->apiUrl . '/v1/chat/completions');
        curl_setopt_array($ch, [
            CURLOPT_RETURNTRANSFER => true,
            CURLOPT_POST           => true,
            CURLOPT_POSTFIELDS     => $payload,
            CURLOPT_HTTPHEADER     => ['Content-Type: application/json'],
            CURLOPT_TIMEOUT        => 60,
        ]);

        $response = curl_exec($ch);
        curl_close($ch);

        $data = json_decode($response, true);
        return $data['choices'][0]['message']['content'] ?? 'Error';
    }
}

$surge    = new SnapgateSurge('http://your-server:8000');
$response = $surge->chat('Write a PHP function to validate an email address');
echo $response;

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Deploy with Ollama

curl -fsSL https://ollama.ai/install.sh | sh

cat > Modelfile << 'EOF'
FROM snapgate-ai/snapgate-surge
SYSTEM """You are Surge, an AI assistant from Snapgate AI (snapgate.tech).
You help users with coding, web development, and general questions."""
PARAMETER temperature 0.7
PARAMETER top_p 0.9
EOF

ollama create snapgate-surge -f Modelfile
ollama run snapgate-surge

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Deploy with vLLM (Production)

pip install vllm

python -m vllm.entrypoints.openai.api_server \
    --model snapgate-ai/snapgate-surge \
    --host 0.0.0.0 \
    --port 8000 \
    --max-model-len 2048

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💡 Example Conversation

English: