Instructions to use RthItalia/PINDARO-AI-CODE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RthItalia/PINDARO-AI-CODE with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="RthItalia/PINDARO-AI-CODE")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RthItalia/PINDARO-AI-CODE")
model = AutoModelForCausalLM.from_pretrained("RthItalia/PINDARO-AI-CODE", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use RthItalia/PINDARO-AI-CODE 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 RthItalia/PINDARO-AI-CODE:F16
# Run inference directly in the terminal:
llama cli -hf RthItalia/PINDARO-AI-CODE:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RthItalia/PINDARO-AI-CODE:F16
# Run inference directly in the terminal:
llama cli -hf RthItalia/PINDARO-AI-CODE:F16

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 RthItalia/PINDARO-AI-CODE:F16
# Run inference directly in the terminal:
./llama-cli -hf RthItalia/PINDARO-AI-CODE:F16

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 RthItalia/PINDARO-AI-CODE:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RthItalia/PINDARO-AI-CODE:F16

Use Docker

docker model run hf.co/RthItalia/PINDARO-AI-CODE:F16

LM Studio
Jan

vLLM

How to use RthItalia/PINDARO-AI-CODE with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RthItalia/PINDARO-AI-CODE"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RthItalia/PINDARO-AI-CODE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/RthItalia/PINDARO-AI-CODE:F16

SGLang

How to use RthItalia/PINDARO-AI-CODE 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 "RthItalia/PINDARO-AI-CODE" \
    --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": "RthItalia/PINDARO-AI-CODE",
		"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 "RthItalia/PINDARO-AI-CODE" \
        --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": "RthItalia/PINDARO-AI-CODE",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use RthItalia/PINDARO-AI-CODE with Ollama:
```
ollama run hf.co/RthItalia/PINDARO-AI-CODE:F16
```
Unsloth Desktop
Docker Model Runner
How to use RthItalia/PINDARO-AI-CODE with Docker Model Runner:
```
docker model run hf.co/RthItalia/PINDARO-AI-CODE:F16
```

Lemonade

How to use RthItalia/PINDARO-AI-CODE with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RthItalia/PINDARO-AI-CODE:F16

Run and chat with the model

lemonade run user.PINDARO-AI-CODE-F16

List all available models

lemonade list

Atomic Chat

RthItalia commited on Mar 2

Commit

722f9dc

verified ·

1 Parent(s): b72aa93

Release AICE-v1: release/RELEASE_MANIFEST.json

Browse files

Files changed (1) hide show

release/RELEASE_MANIFEST.json +63 -0

release/RELEASE_MANIFEST.json ADDED Viewed

	@@ -0,0 +1,63 @@

+{
+  "release_date": "2026-03-02 23:33:21",
+  "model": "PINDARO-AI-CODE",
+  "source_path": "e:\\Pindaro\\PINDARO AI CODE",
+  "publishable": true,
+  "verdict": {
+    "hf_ok": true,
+    "gguf_f16_ok": true,
+    "gguf_q4_ok": true,
+    "publishable": true,
+    "thresholds": {
+      "hf_quality_min": 0.8,
+      "gguf_quality_min": 0.67
+    }
+  },
+  "artifacts": {
+    "model.safetensors": {
+      "size": 2200136048,
+      "sha256": "f77c27b8babf9fcab83a7dc68ba58934e8c8c031c9f10b4b73e802d4fbfe0cec"
+    },
+    "config.json": {
+      "size": 687,
+      "sha256": "b37c45060f3e2f5f9b91903c9ccb32f3c21076e809954fda6c01d987cd8f25cc"
+    },
+    "generation_config.json": {
+      "size": 124,
+      "sha256": "6ff47e725c0ec6d0f1895670de7ee68e61a4f99703f6c8e89aea6ab14ea02dc3"
+    },
+    "tokenizer.json": {
+      "size": 1843262,
+      "sha256": "51433f06369ac3e597dfa23a811215e3511b8f86588a830ded72344b76a193ee"
+    },
+    "tokenizer.model": {
+      "size": 499723,
+      "sha256": "9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347"
+    },
+    "tokenizer_config.json": {
+      "size": 1830,
+      "sha256": "a0567c49a117af9af332874cfd333ddd622a09c5e9765131ceee6344cb22a3de"
+    },
+    "special_tokens_map.json": {
+      "size": 621,
+      "sha256": "d7805e093432afcde852968cdeba3de08a6fe66e77609f4701decb87fc492f33"
+    },
+    "added_tokens.json": {
+      "size": 46,
+      "sha256": "ece349d292e246eac9a9072c1730f023e61567984a828fb0d25dccb14e3b7592"
+    },
+    "pindaro-f16.gguf": {
+      "size": 2201034080,
+      "sha256": "bdaaeb6fb712e9a4d952082cf415b05c7d076b33786d39063bbfb3a7e5db2031"
+    },
+    "pindaro-q4_k_m.gguf": {
+      "size": 667820928,
+      "sha256": "5f98cc3454774ed5ed80d71a71adfd0daff760fc9eef0900ddd4f7eda2e20fef"
+    }
+  },
+  "paths": {
+    "root": "e:\\Pindaro\\PINDARO AI CODE",
+    "gguf_f16": "e:\\Pindaro\\PINDARO AI CODE\\pindaro-f16.gguf",
+    "gguf_q4": "e:\\Pindaro\\PINDARO AI CODE\\pindaro-q4_k_m.gguf"
+  }
+}