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

b71b891

verified ·

1 Parent(s): 3abb941

Fix card: declare GGUF artifacts and checksums

Browse files

Files changed (1) hide show

README.md +8 -2

README.md CHANGED Viewed

@@ -21,7 +21,8 @@ Model path: `e:\Pindaro\PINDARO AI CODE`
 - Intended role: coding assistant
 - Format support:
   - Hugging Face (`model.safetensors`)
-  - No GGUF artifact in this folder
 ## 2. Technical Specs
 - Architecture: `LlamaForCausalLM`
@@ -62,8 +63,13 @@ Configured template:
 - `model.safetensors`: `F77C27B8BABF9FCAB83A7DC68BA58934E8C8C031C9F10B4B73E802D4FBFE0CEC`
 - `config.json`: `B37C45060F3E2F5F9B91903C9CCB32F3C21076E809954FDA6C01D987CD8F25CC`
 - `generation_config.json`: `6FF47E725C0EC6D0F1895670DE7EE68E61A4F99703F6C8E89AEA6AB14EA02DC3`
-- `tokenizer_config.json`: `4AE48D4963835B0767DC6510D1DF8AE76E636ED35BE021F02F5B344353BDFA21`
 - `tokenizer.model`: `9E556AFD44213B6BD1BE2B850EBBBD98F5481437A8021AFAF58EE7FB1818D347`
 ## 5. Smoke Tests (2026-03-02)
 Environment:

 - Intended role: coding assistant
 - Format support:
   - Hugging Face (`model.safetensors`)
+  - GGUF F16 (`pindaro-f16.gguf`)
+  - GGUF Q4_K_M (`pindaro-q4_k_m.gguf`)
 ## 2. Technical Specs
 - Architecture: `LlamaForCausalLM`
 - `model.safetensors`: `F77C27B8BABF9FCAB83A7DC68BA58934E8C8C031C9F10B4B73E802D4FBFE0CEC`
 - `config.json`: `B37C45060F3E2F5F9B91903C9CCB32F3C21076E809954FDA6C01D987CD8F25CC`
 - `generation_config.json`: `6FF47E725C0EC6D0F1895670DE7EE68E61A4F99703F6C8E89AEA6AB14EA02DC3`
+- `tokenizer.json`: `51433F06369AC3E597DFA23A811215E3511B8F86588A830DED72344B76A193EE`
+- `tokenizer_config.json`: `A0567C49A117AF9AF332874CFD333DDD622A09C5E9765131CEEE6344CB22A3DE`
 - `tokenizer.model`: `9E556AFD44213B6BD1BE2B850EBBBD98F5481437A8021AFAF58EE7FB1818D347`
+- `special_tokens_map.json`: `D7805E093432AFCDE852968CDEBA3DE08A6FE66E77609F4701DECB87FC492F33`
+- `added_tokens.json`: `ECE349D292E246EAC9A9072C1730F023E61567984A828FB0D25DCCB14E3B7592`
+- `pindaro-f16.gguf`: `BDAAEB6FB712E9A4D952082CF415B05C7D076B33786D39063BBFB3A7E5DB2031`
+- `pindaro-q4_k_m.gguf`: `5F98CC3454774ED5ED80D71A71ADFD0DAFF760FC9EEF0900DDD4F7EDA2E20FEF`
 ## 5. Smoke Tests (2026-03-02)
 Environment: