Flexan/RthItalia-PINDARO-AI-CODE-GGUF

GGUF Files for PINDARO-AI-CODE

These are the GGUF files for RthItalia/PINDARO-AI-CODE.

Downloads

GGUF Link	Quantization	Description
Download	Q2_K	Lowest quality
Download	Q3_K_S
Download	IQ3_S	Integer quant, preferable over Q3_K_S
Download	IQ3_M	Integer quant
Download	Q3_K_M
Download	Q3_K_L
Download	IQ4_XS	Integer quant
Download	Q4_K_S	Fast with good performance
Download	Q4_K_M	Recommended: Perfect mix of speed and performance
Download	Q5_K_S
Download	Q5_K_M
Download	Q6_K	Very good quality
Download	Q8_0	Best quality
Download	f16	Full precision, don't bother; use a quant

Note from Flexan

I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet, usually for models I deem interesting and wish to try out.

If there are some quants missing that you'd like me to add, you may request one in the community tab. If you want to request a public model to be converted, you can also request that in the community tab. If you have questions regarding this model, please refer to the original model repo.

You can find more info about me and what I do here.

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)
  • GGUF F16 (pindaro-f16.gguf)
  • GGUF Q4_K_M (pindaro-q4_k_m.gguf)

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:

{{ 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.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:

• 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

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.