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

b72aa93

verified ·

1 Parent(s): 18f9e9a

Release AICE-v1: README

Browse files

Files changed (1) hide show

README.md +133 -0

README.md ADDED Viewed

	@@ -0,0 +1,133 @@

+---
+language:
+- en
+- it
+pipeline_tag: text-generation
+tags:
+- gguf
+- code
+- instruct
+- llama
+---
+# MODEL_CARD - PINDARO AI CODE
+Date: 2026-03-02
+Model path: `e:\Pindaro\PINDARO AI CODE`
+## 1. Model Identity
+- Name: `PINDARO AI CODE`
+- Family: LLaMA-style causal LM
+- Intended role: coding assistant
+- Format support:
+  - Hugging Face (`model.safetensors`)
+  - No GGUF artifact in this folder
+## 2. Technical Specs
+- Architecture: `LlamaForCausalLM`
+- `model_type`: `llama`
+- Layers: `22`
+- Hidden size: `2048`
+- Attention heads: `32`
+- KV heads: `4`
+- Intermediate size: `5632`
+- Max context: `2048`
+- Vocab size: `32002`
+- Tensor count in safetensors: `201`
+- Parameter count (computed): `1,100,056,576`
+- Dtype in config: `float16`
+## 3. Chat / Prompt Format
+Template is aligned to registered special tokens:
+- `<|noesis|>` (id `32000`)
+- `<|end|>` (id `32001`)
+Configured template:
+````jinja
+{{ bos_token }}{% for message in messages %}<|noesis|>
+{% if message['role'] == 'system' %}### System
+{{ message['content'] }}
+{% elif message['role'] == 'user' %}### Question
+{{ message['content'] }}
+{% elif message['role'] == 'assistant' %}### Answer
+{{ message['content'] }}
+{% endif %}<|end|>
+{% endfor %}{% if add_generation_prompt %}<|noesis|>
+### Answer
+```
+{% endif %}
+````
+## 4. Local Artifact Integrity (SHA256)
+- `model.safetensors`: `F77C27B8BABF9FCAB83A7DC68BA58934E8C8C031C9F10B4B73E802D4FBFE0CEC`
+- `config.json`: `B37C45060F3E2F5F9B91903C9CCB32F3C21076E809954FDA6C01D987CD8F25CC`
+- `generation_config.json`: `6FF47E725C0EC6D0F1895670DE7EE68E61A4F99703F6C8E89AEA6AB14EA02DC3`
+- `tokenizer_config.json`: `4AE48D4963835B0767DC6510D1DF8AE76E636ED35BE021F02F5B344353BDFA21`
+- `tokenizer.model`: `9E556AFD44213B6BD1BE2B850EBBBD98F5481437A8021AFAF58EE7FB1818D347`
+## 5. Smoke Tests (2026-03-02)
+Environment:
+- Python `3.11.9`
+- Transformers `4.57.3`
+- Torch `2.10.0+cpu`
+Results:
+- AutoConfig load: PASS
+- AutoTokenizer load: PASS
+- AutoModel load: PASS
+- Chat-template render: PASS
+- Template special-token alignment: PASS
+- Deterministic generation: PASS
+Observed non-blocking warning:
+- Folder name with spaces may trigger a Python module-name warning in some runtimes.
+## 6. Known Issues
+1. Folder-name warning risk
+- `PINDARO AI CODE` has spaces; some tools warn on module naming.
+2. Attention-mask warning in some calls
+- As `pad_token` equals `eos_token`, pass `attention_mask` explicitly for stable behavior.
+## 7. Recommended Next Steps
+1. Optional packaging cleanup
+- Rename folder to a no-space slug (example: `PINDARO_AI_CODE`) when compatible with your deployment scripts.
+2. Add coding eval gate
+- HumanEval pass@1
+- MBPP subset
+- Prompt-format adherence checks
+## 8. Usage Example
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+path = r"e:\Pindaro\PINDARO AI CODE"
+tokenizer = AutoTokenizer.from_pretrained(path, local_files_only=True)
+model = AutoModelForCausalLM.from_pretrained(path, local_files_only=True, dtype=torch.float16)
+messages = [
+    {"role": "system", "content": "You are a coding assistant."},
+    {"role": "user", "content": "Write a Python function add(a, b)."},
+]
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt",
+)
+outputs = model.generate(inputs, max_new_tokens=80, do_sample=False)
+print(tokenizer.decode(outputs[0], skip_special_tokens=False))
+```
+## 9. Limitations and Safety
+- No training-data statement is included in this folder.
+- No official benchmark sheet is included.
+- Code generation can be plausible but wrong; always run tests.
+## 10. Release Readiness
+Current status: READY FOR LOCAL USE.
+- Packaging/runtime blockers are resolved.
+- Remaining items are evaluation and packaging polish.