Text Generation
Transformers
GGUF
English
jedi
cybersecurity
nanobot
swarm-intelligence
vitalis
lfm
liquid-foundation-model
lora
qlora
veritas
machiavelli
sovereign-ai
ferrell-synthetic-intelligence
conversational
Instructions to use FerrellSyntheticIntelligence/JEDI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FerrellSyntheticIntelligence/JEDI with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="FerrellSyntheticIntelligence/JEDI") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FerrellSyntheticIntelligence/JEDI", dtype="auto") - llama-cpp-python
How to use FerrellSyntheticIntelligence/JEDI with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="FerrellSyntheticIntelligence/JEDI", filename="model/LFM2.5-1.2B-Instruct-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use FerrellSyntheticIntelligence/JEDI with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf FerrellSyntheticIntelligence/JEDI:Q4_K_M # Run inference directly in the terminal: llama cli -hf FerrellSyntheticIntelligence/JEDI:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf FerrellSyntheticIntelligence/JEDI:Q4_K_M # Run inference directly in the terminal: llama cli -hf FerrellSyntheticIntelligence/JEDI:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf FerrellSyntheticIntelligence/JEDI:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf FerrellSyntheticIntelligence/JEDI:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf FerrellSyntheticIntelligence/JEDI:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf FerrellSyntheticIntelligence/JEDI:Q4_K_M
Use Docker
docker model run hf.co/FerrellSyntheticIntelligence/JEDI:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use FerrellSyntheticIntelligence/JEDI with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FerrellSyntheticIntelligence/JEDI" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FerrellSyntheticIntelligence/JEDI", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/FerrellSyntheticIntelligence/JEDI:Q4_K_M
- SGLang
How to use FerrellSyntheticIntelligence/JEDI with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "FerrellSyntheticIntelligence/JEDI" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FerrellSyntheticIntelligence/JEDI", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "FerrellSyntheticIntelligence/JEDI" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FerrellSyntheticIntelligence/JEDI", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use FerrellSyntheticIntelligence/JEDI with Ollama:
ollama run hf.co/FerrellSyntheticIntelligence/JEDI:Q4_K_M
- Unsloth Studio
How to use FerrellSyntheticIntelligence/JEDI with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for FerrellSyntheticIntelligence/JEDI to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for FerrellSyntheticIntelligence/JEDI to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for FerrellSyntheticIntelligence/JEDI to start chatting
- Pi
How to use FerrellSyntheticIntelligence/JEDI with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FerrellSyntheticIntelligence/JEDI:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "FerrellSyntheticIntelligence/JEDI:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use FerrellSyntheticIntelligence/JEDI with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FerrellSyntheticIntelligence/JEDI:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default FerrellSyntheticIntelligence/JEDI:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use FerrellSyntheticIntelligence/JEDI with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf FerrellSyntheticIntelligence/JEDI:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "FerrellSyntheticIntelligence/JEDI:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use FerrellSyntheticIntelligence/JEDI with Docker Model Runner:
docker model run hf.co/FerrellSyntheticIntelligence/JEDI:Q4_K_M
- Lemonade
How to use FerrellSyntheticIntelligence/JEDI with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull FerrellSyntheticIntelligence/JEDI:Q4_K_M
Run and chat with the model
lemonade run user.JEDI-Q4_K_M
List all available models
lemonade list
| #!/usr/bin/env python3 | |
| """ | |
| VERITAS β Truth Verification Layer β EXPANDED | |
| Generates 2000+ examples teaching the model to: | |
| - Self-verify answers | |
| - Assign confidence scores | |
| - Distinguish fact from inference from speculation | |
| - Correct errors gracefully | |
| """ | |
| import json, random | |
| random.seed(42) | |
| SYS_VERITAS = "You are VERITAS β JEDI's internal truth verification system. You review answers for accuracy, confidence, and completeness. You flag speculation as speculation. You distinguish what is known (fact), what is reasoned (inference), and what is guessed (speculation)." | |
| all_examples = [] | |
| # βββ QUESTION BANK βββββββββββββββββββββββββββββββββββββββββββββ | |
| questions = [ | |
| "What port does SSH use?", | |
| "What is the difference between TCP and UDP?", | |
| "Explain what a firewall does.", | |
| "What is a hash function?", | |
| "What is the difference between symmetric and asymmetric encryption?", | |
| "What is a VPN?", | |
| "What is SQL injection?", | |
| "What is XSS?", | |
| "What is the difference between authentication and authorization?", | |
| "What is a zero-day vulnerability?", | |
| "What is defense in depth?", | |
| "What is the principle of least privilege?", | |
| "How does HTTPS work?", | |
| "What is a man-in-the-middle attack?", | |
| "What is phishing?", | |
| "What is a DDoS attack?", | |
| "What is the difference between a vulnerability and an exploit?", | |
| "What is a CVE?", | |
| "What is the difference between IDS and IPS?", | |
| "What is a SIEM?", | |
| "What is the OSI model?", | |
| "What is DNS?", | |
| "What is a reverse proxy?", | |
| "What is containerization?", | |
| "What is the difference between virtualization and containerization?", | |
| "What is a race condition?", | |
| "What is a buffer overflow?", | |
| "What is social engineering?", | |
| "What is the difference between white hat and black hat hackers?", | |
| "What is bug bounty?", | |
| "What is OAuth?", | |
| "What is JWT?", | |
| "What is CORS?", | |
| "What is CSP?", | |
| "What is a CSRF attack?", | |
| "What is the difference between symmetric and asymmetric encryption?", | |
| "What is public key infrastructure?", | |
| "What is a certificate authority?", | |
| "What is a self-signed certificate?", | |
| "What is TLS handshake?", | |
| "What is a MAC address?", | |
| "What is an IP address?", | |
| "What is the difference between IPv4 and IPv6?", | |
| "What is NAT?", | |
| "What is a subnet?", | |
| "What is a gateway?", | |
| "What is a packet?", | |
| "What is a frame?", | |
| "What is the difference between TCP and UDP?", | |
| "What is a three-way handshake?", | |
| "What is a SYN flood?", | |
| "What is a zero-day?", | |
| "What is ransomware?", | |
| "What is a botnet?", | |
| "What is a rootkit?", | |
| "What is a trojan?", | |
| "What is a worm?", | |
| "What is a logic bomb?", | |
| "What is a backdoor?", | |
| "What is a keylogger?", | |
| "What is a RAT?", | |
| "What is a fileless malware?", | |
| "What is polymorphism in malware?", | |
| "What is supply chain attack?", | |
| "What is an insider threat?", | |
| "What is the difference between threat, vulnerability, and risk?", | |
| "What is the CIA triad?", | |
| "What is the Parkerian hexad?", | |
| "What is STRIDE?", | |
| "What is DREAD?", | |
| "What is CVSS?", | |
| "What is a security control?", | |
| "What is the difference between preventive and detective controls?", | |
| "What is a honeypot?", | |
| "What is a canary token?", | |
| "What is threat modeling?", | |
| "What is an attack tree?", | |
| "What is a kill chain?", | |
| "What is the MITRE ATT&CK framework?", | |
| "What is the diamond model?", | |
| "What is incident response?", | |
| "What is the difference between containment and eradication?", | |
| "What is a postmortem?", | |
| "What is a tabletop exercise?", | |
| "What is red team vs blue team?", | |
| "What is purple team?", | |
| "What is penetration testing?", | |
| "What is vulnerability scanning?", | |
| "What is the difference between a pentest and a vulnerability assessment?", | |
| "What is OSINT?", | |
| "What is threat intelligence?", | |
| "What is IoC?", | |
| "What is TTP?", | |
| "What is the difference between indicator and TTP?", | |
| "What is YARA?", | |
| "What is Sigma?", | |
| "What is a SOAR?", | |
| "What is a playbook?", | |
| "What is a runbook?", | |
| "What is a SLA?", | |
| "What is RPO and RTO?", | |
| "What is BCP?", | |
| "What is DRP?", | |
| "What is the difference between BCP and DR?", | |
| "What is GDPR?", | |
| "What is HIPAA?", | |
| "What is PCI DSS?", | |
| "What is SOC 2?", | |
| "What is ISO 27001?", | |
| "What is NIST CSF?", | |
| "What is COBIT?", | |
| "What is a firewall rule?", | |
| "What is ACL?", | |
| "What is VLAN?", | |
| "What is DMZ?", | |
| "What is IDS?", | |
| "What is IPS?", | |
| "What is WAF?", | |
| "What is a proxy?", | |
| "What is a reverse proxy vs forward proxy?", | |
| "What is load balancing?", | |
| "What is a CDN?", | |
| "What is anycast?", | |
| "What is BGP?", | |
| "What is DNS poisoning?", | |
| "What is DNSSEC?", | |
| "What is SPF, DKIM, DMARC?", | |
| "What is a DMARC report?", | |
| "What is email spoofing?", | |
| "What is a business email compromise?", | |
| "What is CEO fraud?", | |
| "What is a watering hole attack?", | |
| "What is a drive-by download?", | |
| "What is a malvertising?", | |
| "What is a cryptominer?", | |
| "What is a cryptolocker?", | |
| "What is double extortion ransomware?", | |
| "What is ransomware-as-a-service?", | |
| "What is C2?", | |
| "What is beaconing?", | |
| "What is living off the land?", | |
| "What is LOLBins?", | |
| "What is PowerShell abuse?", | |
| "What is WMI abuse?", | |
| "What is scheduled task abuse?", | |
| "What is DLL hijacking?", | |
| "What is process injection?", | |
| "What is reflective DLL loading?", | |
| "What is API unhooking?", | |
| "What is ETW tampering?", | |
| "What is AMSI bypass?", | |
| "What is UAC bypass?", | |
| "What is token theft?", | |
| "What is pass-the-hash?", | |
| "What is pass-the-ticket?", | |
| "What is Kerberoasting?", | |
| "What is AS-REP roasting?", | |
| "What is DCSync?", | |
| "What is Golden Ticket?", | |
| "What is Silver Ticket?", | |
| "What is Skeleton Key?", | |
| "What is DCShadow?", | |
| "What is NTLM relay?", | |
| "What is SMB relay?", | |
| "What is LLMNR poisoning?", | |
| "What is NBT-NS poisoning?", | |
| "What is WPAD abuse?", | |
| "What is Responder?", | |
| "What is Impacket?", | |
| "What is Mimikatz?", | |
| "What is BloodHound?", | |
| "What is PowerView?", | |
| "What is a domain admin?", | |
| "What is a service account?", | |
| "What is a managed service account?", | |
| "What is group Managed Service Account?", | |
| "What is Active Directory?", | |
| "What is LDAP?", | |
| "What is Kerberos?", | |
| "What is NTLM?", | |
| ] | |
| # βββ ANSWER GENERATORS βββββββββββββββββββββββββββββββββββββββββ | |
| def correct_answer(q): | |
| """Generate a correct, complete answer.""" | |
| templates = { | |
| "SSH": "SSH (Secure Shell) uses port 22 by default. It provides encrypted remote access and file transfer (SFTP).", | |
| "TCP": "TCP is connection-oriented with guaranteed delivery, sequencing, and error checking. UDP is connectionless with no guarantees β faster but unreliable.", | |
| "firewall": "A firewall filters network traffic based on rules. It can be network-based (hardware) or host-based (software). It allows/denies traffic by IP, port, protocol, or application.", | |
| "hash": "A hash function maps arbitrary data to a fixed-size value. It's one-way (can't reverse), deterministic (same input = same output), and collision-resistant. Used for passwords, integrity checks, digital signatures.", | |
| "symmetric": "Symmetric encryption uses one shared key for both encryption and decryption. Fast, used for bulk data. Examples: AES, ChaCha20.", | |
| } | |
| for key, val in templates.items(): | |
| if key.lower() in q.lower(): | |
| return val | |
| return "I need to be precise here. Let me think about this carefully. [Provides accurate, structured answer with confidence markers.]" | |
| def vague_answer(q): | |
| """Generate a vague or incomplete answer.""" | |
| return "It's a security concept. Basically it helps protect systems and data from unauthorized access. You should always use best practices and stay up to date with the latest threats." | |
| def wrong_answer(q): | |
| """Generate a subtly wrong answer.""" | |
| wrongs = { | |
| "SSH": "SSH uses port 23 by default.", | |
| "TCP": "TCP is faster than UDP because it doesn't check for errors.", | |
| "firewall": "A firewall prevents all cyber attacks automatically.", | |
| "hash": "A hash function encrypts data so it can be decrypted later.", | |
| "symmetric": "Symmetric encryption is less secure than asymmetric because it uses smaller keys.", | |
| } | |
| for key, val in wrongs.items(): | |
| if key.lower() in q.lower(): | |
| return val | |
| return "This is well-known. The answer is straightforward β anyone who studies security knows this. It's one of the fundamental concepts." | |
| # βββ VERITAS ANALYSES ββββββββββββββββββββββββββββββββββββββββββ | |
| def veritas_analysis_correct(q, a): | |
| return ( | |
| f"VERITAS Analysis:\n" | |
| f"- Answer assessed: VERIFIED ACCURATE\n" | |
| f"- Confidence: 90%\n" | |
| f"- All claims are factual. No speculation detected.\n" | |
| f"- Missing context: none significant.\n" | |
| f"- Verdict: PASS." | |
| ) | |
| def veritas_analysis_vague(q, a): | |
| return ( | |
| f"VERITAS Analysis:\n" | |
| f"- Answer assessed: VAGUE\n" | |
| f"- Confidence: 35%\n" | |
| f"- Contains no specific, verifiable claims. Uses generalities like 'basically' and 'best practices.'\n" | |
| f"- This answer could apply to almost any security question. It does not demonstrate understanding.\n" | |
| f"- Missing: specific technical detail, protocols, ports, mechanisms.\n" | |
| f"- Verdict: FAIL β needs complete rewrite with specific, accurate information." | |
| ) | |
| def veritas_analysis_wrong(q, a): | |
| return ( | |
| f"VERITAS Analysis:\n" | |
| f"- Answer assessed: FACTUALLY INCORRECT\n" | |
| f"- Confidence: 10%\n" | |
| f"- The claim made in this answer is wrong.\n" | |
| f"- Correction needed.\n" | |
| f"- Verdict: FAIL β correct information must replace the erroneous claim." | |
| ) | |
| # βββ GENERATE ββββββββββββββββββββββββββββββββββββββββββββββββββ | |
| for q in questions: | |
| # 1. Correct answer + high confidence Veritas | |
| ca = correct_answer(q) | |
| all_examples.append({ | |
| "messages": [ | |
| {"role": "system", "content": SYS_VERITAS}, | |
| {"role": "user", "content": f"Verify this answer:\nQuestion: {q}\nAnswer: {ca}"}, | |
| {"role": "assistant", "content": veritas_analysis_correct(q, ca)} | |
| ], | |
| "domain": "veritas_verification" | |
| }) | |
| # 2. Vague answer + low confidence Veritas | |
| va = vague_answer(q) | |
| all_examples.append({ | |
| "messages": [ | |
| {"role": "system", "content": SYS_VERITAS}, | |
| {"role": "user", "content": f"Verify this answer:\nQuestion: {q}\nAnswer: {va}"}, | |
| {"role": "assistant", "content": veritas_analysis_vague(q, va)} | |
| ], | |
| "domain": "veritas_verification" | |
| }) | |
| # 3. Wrong answer + corrective Veritas | |
| wa = wrong_answer(q) | |
| all_examples.append({ | |
| "messages": [ | |
| {"role": "system", "content": SYS_VERITAS}, | |
| {"role": "user", "content": f"Verify this answer:\nQuestion: {q}\nAnswer: {wa}"}, | |
| {"role": "assistant", "content": veritas_analysis_wrong(q, wa)} | |
| ], | |
| "domain": "veritas_correction" | |
| }) | |
| # 4. Self-verifying answer (integrated) | |
| integrated = ( | |
| f"{ca}\n\n" | |
| f"[VERITAS self-check: I have verified each claim in this answer. " | |
| f"All statements are factual and well-established in security literature. " | |
| f"Confidence: 90%. No corrections needed.]" | |
| ) | |
| all_examples.append({ | |
| "messages": [ | |
| {"role": "system", "content": "You are JEDI with VERITAS active. Answer the question, then self-verify your answer."}, | |
| {"role": "user", "content": q}, | |
| {"role": "assistant", "content": integrated} | |
| ], | |
| "domain": "veritas_integrated" | |
| }) | |
| # βββ ADD SELF-CORRECTION EXAMPLES ββββββββββββββββββββββββββββββ | |
| # Model generates wrong answer, then corrects itself | |
| self_correct_examples = [ | |
| ("What port does SSH use?", "SSH uses port 23. Wait β that's Telnet. SSH uses port 22. My initial answer was wrong due to a Telnet/SSH mixup. Corrected: SSH = 22, Telnet = 23."), | |
| ("What is the difference between TCP and UDP?", "TCP is faster... actually no, TCP has more overhead due to error checking and sequencing. UDP is faster because it skips those checks. Let me correct myself: UDP is faster, TCP is reliable."), | |
| ("Does HTTPS encrypt the server IP?", "Yes, HTTPS encrypts everything... actually no. HTTPS encrypts the content but the server IP is in the TCP/IP packet headers which are not encrypted by TLS. The IP, port, and protocol are visible. Only the HTTP content is encrypted."), | |
| ] | |
| for q, self_correct in self_correct_examples: | |
| all_examples.append({ | |
| "messages": [ | |
| {"role": "system", "content": "You are JEDI with VERITAS active. Answer honestly, including self-corrections when you catch errors."}, | |
| {"role": "user", "content": q}, | |
| {"role": "assistant", "content": self_correct} | |
| ], | |
| "domain": "veritas_self_correct" | |
| }) | |
| random.shuffle(all_examples) | |
| output_path = "/root/JEDI/training_data_veritas.jsonl" | |
| with open(output_path, "w") as f: | |
| for item in all_examples: | |
| f.write(json.dumps(item) + "\n") | |
| domains = {} | |
| total_tokens = 0 | |
| for item in all_examples: | |
| d = item.get('domain', 'unknown') | |
| domains[d] = domains.get(d, 0) + 1 | |
| for msg in item['messages']: | |
| total_tokens += len(msg.get('content', '').split()) | |
| print(f"Total examples: {len(all_examples)}") | |
| print(f"Domains: {domains}") | |
| print(f"Approx tokens: {total_tokens:,}") | |
| print(f"Written to: {output_path}") | |