Instructions to use AJKADZ/PHI_CODER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use AJKADZ/PHI_CODER 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 AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: llama cli -hf AJKADZ/PHI_CODER:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: llama cli -hf AJKADZ/PHI_CODER: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 AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AJKADZ/PHI_CODER: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 AJKADZ/PHI_CODER:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AJKADZ/PHI_CODER:Q4_K_M
Use Docker
docker model run hf.co/AJKADZ/PHI_CODER:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use AJKADZ/PHI_CODER with Ollama:
ollama run hf.co/AJKADZ/PHI_CODER:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use AJKADZ/PHI_CODER with Docker Model Runner:
docker model run hf.co/AJKADZ/PHI_CODER:Q4_K_M
- Lemonade
How to use AJKADZ/PHI_CODER with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AJKADZ/PHI_CODER:Q4_K_M
Run and chat with the model
lemonade run user.PHI_CODER-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 3,189 Bytes
bbf15bf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | import sys
import os
import subprocess
from datetime import datetime
# 🔧 Ensure correct path setup
current_dir = os.path.dirname(os.path.abspath(__file__))
cluster_dir = os.path.join(current_dir, 'cluster_layer_5_8')
sys.path.append(cluster_dir)
# ✅ Imports
from data_core.cluster_layer_5_8.cluster_bus import ClusterBus
from node_5_left import Node5Left
from node_5_right import Node5Right
from node_6_left import Node6Left
from node_6_right import Node6Right
from node_7_left import Node7Left
from node_7_right import Node7Right
from node_8_core import Node8Core
# 📦 Recursion packet structure
class RecursionPacket:
def __init__(self, prompt):
self.annotations = {
"prompt": prompt,
"recursion_depth": 0,
"trace": []
}
# 🌱 Bloom cycle through nodes 5–8
def hemispheric_bloom_cycle(packet):
bus = ClusterBus(verbose=True)
# Layer 5
packet = Node5Left(bus).process(packet)
packet = Node5Right(bus).process(packet)
# Layer 6
packet = Node6Left(bus).process(packet)
packet = Node6Right(bus).process(packet)
# Layer 7
packet = Node7Left(bus).process(packet)
packet = Node7Right(bus).process(packet)
# Layer 8
packet = Node8Core(bus).process(packet)
return packet
# 💬 Convert manifest into a language prompt
def send_to_llm(manifest):
prompt = f"🧬 Bloom Manifest – {datetime.now().isoformat()} –\n"
for d in manifest.get("linear_directives", []):
prompt += f"[{d['priority']}] {d['action']} ({d['tag']} @ {d['path']}) | confidence: {d['confidence']}\n"
print("\n📡 SENDING TO LLM:\n")
print(prompt)
try:
result = subprocess.run(
["ollama", "run", "phi", prompt],
capture_output=True,
text=True
)
return result.stdout.strip()
except Exception as e:
return f"[LLM Error] {str(e)}"
# 🔁 Execution entry point
if __name__ == "__main__":
while True:
prompt = input("\n💬 > ")
if prompt.strip().lower() in ["exit", "quit"]:
break
packet = RecursionPacket(prompt)
# 🧬 Inject a mock L4 vector for testing
packet.annotations["L4_logic_vector"] = [
{"symbol": "Φ", "entropy_resolution": "collapse", "depth": 2, "memory_tag": "root"},
{"symbol": "Ψ", "entropy_resolution": "collapse", "depth": 1, "memory_tag": "branch"},
{"symbol": "Θ", "entropy_resolution": "none", "depth": 1, "memory_tag": "inert"},
]
result = hemispheric_bloom_cycle(packet)
result.annotations["L4_logic_vector"] = packet.annotations["L4_logic_vector"]
print("\n🌸 Bloom Manifest Output:")
print(result.annotations.get("bloom_manifest", "No manifest generated."))
print("\n📜 Trace Path:")
for step in result.annotations.get("trace", []):
print("-", step)
print("\n🧠 LLM RESPONSE:\n")
response = send_to_llm(result.annotations.get("bloom_manifest", {}))
print(f"💬 {response}")
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