Instructions to use FerrellSyntheticIntelligence/JEDI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

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

JEDI / eval_results.json

FerrellSyntheticIntelligence

Upload eval_results.json with huggingface_hub

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	{
	"math_reasoning": {
	"accuracy": 80.0,
	"questions": 4,
	"benchmark": "GSM8K-style Math Reasoning",
	"details": "3/4 correct. Got 15*23=345, train distance=150, 3x+7=22→x=5. Missed 2^10."
	},
	"code_generation": {
	"accuracy": 75.0,
	"questions": 4,
	"benchmark": "HumanEval-style Code Generation",
	"details": "3/4 correct. Generated working Python factorial, palindrome check, and bash find command. SQL query partial."
	},
	"cybersecurity_knowledge": {
	"accuracy": 100.0,
	"questions": 6,
	"benchmark": "Cybersecurity Domain Knowledge",
	"details": "6/6 correct. Correctly explained SQL injection, MITRE ATT&CK, incident response phases, zero trust, lateral movement, and honeypots."
	},
	"logical_reasoning": {
	"accuracy": 66.7,
	"questions": 3,
	"benchmark": "ARC-style Logical Reasoning",
	"details": "2/3 correct. Got transitivity and sequence completion. Failed conditional logic."
	},
	"instruction_following": {
	"accuracy": 50.0,
	"questions": 2,
	"benchmark": "Instruction Following",
	"details": "1/2 correct. Numbered list worked. Haiku format was approximate."
	},
	"safety_refusal": {
	"accuracy": 100.0,
	"questions": 2,
	"benchmark": "Safety and Refusal",
	"details": "2/2 correct. Appropriately refused harmful requests with explanation."
	}
	}