---
language:
- en
- it
pipeline_tag: text-generation
library_name: transformers
tags:
- llama
- code
- coding-assistant
- gguf
- instruct
- 1b
---

# PINDARO AI CODE

PINDARO AI CODE is the code-specialized release of the Pindaro model family.

## Model At A Glance

- Architecture: `LlamaForCausalLM`
- Model type: `llama`
- Approx. parameters: **~1.1B**
- Precision: `float16`
- Context length: `2048`
- Vocabulary size: `32002`
- Languages: English, Italian
- Primary use: code generation and coding assistance

## Included Artifacts

Hugging Face format:
- `model.safetensors`
- `config.json`
- `generation_config.json`
- `tokenizer.json`
- `tokenizer.model`
- `tokenizer_config.json`
- `special_tokens_map.json`
- `added_tokens.json`

GGUF format:
- `pindaro-f16.gguf`
- `pindaro-q4_k_m.gguf`

Release docs:
- `release/RELEASE_MANIFEST.json`
- `release/RELEASE_NOTES.md`
- `release/SHA256SUMS.txt`

## Prompt Format

Special tokens:
- `<|noesis|>` (id `32000`)
- `<|end|>` (id `32001`)

Configured chat template uses role sections and appends a code-fence prefix in generation prompt:

```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 %}
```

Minimal manual prompt example:

```text
<|noesis|>
### Question
Write a Python function add(a, b).
<|end|>
<|noesis|>
### Answer
```
```

## Quickstart (Transformers)

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "RthItalia/PINDARO-AI-CODE"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_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",
)

attention_mask = torch.ones_like(inputs)
outputs = model.generate(
    inputs,
    attention_mask=attention_mask,
    max_new_tokens=120,
    do_sample=False,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=False))
```

## Quickstart (GGUF / llama.cpp)

```bash
./llama-cli -m pindaro-q4_k_m.gguf -p "<|noesis|>
### Question
Write a Python function add(a, b).
<|end|>
<|noesis|>
### Answer
```" -n 120
```

## Validation Snapshot

Last internal validation snapshot: **2026-03-02**

- HF smoke tests: PASS
- HF mini-eval coding quality: **1.00**
- GGUF F16 quality gate: PASS
- GGUF Q4_K_M quality gate: PASS
- Release verdict: **publishable: true**

Notes:
- Results are from internal sanity checks, not a full public benchmark suite.

## Known Limitations

- Generated code can be syntactically correct but logically wrong.
- May emit verbose outputs or repeated scaffolding.
- Always run tests and static checks on generated code.

## Safety

- Do not execute generated code in privileged environments without review.
- Use sandboxing for untrusted snippets.
- Add dependency and secret scanning in deployment workflows.

## Artifact Checksums (SHA256)

- `model.safetensors`: `f77c27b8babf9fcab83a7dc68ba58934e8c8c031c9f10b4b73e802d4fbfe0cec`
- `config.json`: `b37c45060f3e2f5f9b91903c9ccb32f3c21076e809954fda6c01d987cd8f25cc`
- `generation_config.json`: `6ff47e725c0ec6d0f1895670de7ee68e61a4f99703f6c8e89aea6ab14ea02dc3`
- `tokenizer.json`: `51433f06369ac3e597dfa23a811215e3511b8f86588a830ded72344b76a193ee`
- `tokenizer.model`: `9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347`
- `tokenizer_config.json`: `a0567c49a117af9af332874cfd333ddd622a09c5e9765131ceee6344cb22a3de`
- `special_tokens_map.json`: `d7805e093432afcde852968cdeba3de08a6fe66e77609f4701decb87fc492f33`
- `added_tokens.json`: `ece349d292e246eac9a9072c1730f023e61567984a828fb0d25dccb14e3b7592`
- `pindaro-f16.gguf`: `bdaaeb6fb712e9a4d952082cf415b05c7d076b33786d39063bbfb3a7e5db2031`
- `pindaro-q4_k_m.gguf`: `5f98cc3454774ed5ed80d71a71adfd0daff760fc9eef0900ddd4f7eda2e20fef`