Instructions to use inaciose/inacio-sft-d16v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inaciose/inacio-sft-d16v1 with Transformers:

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

pipe = pipeline("text-generation", model="inaciose/inacio-sft-d16v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("inaciose/inacio-sft-d16v1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inaciose/inacio-sft-d16v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inaciose/inacio-sft-d16v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inaciose/inacio-sft-d16v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inaciose/inacio-sft-d16v1

SGLang

How to use inaciose/inacio-sft-d16v1 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 "inaciose/inacio-sft-d16v1" \
    --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": "inaciose/inacio-sft-d16v1",
		"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 "inaciose/inacio-sft-d16v1" \
        --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": "inaciose/inacio-sft-d16v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inaciose/inacio-sft-d16v1 with Docker Model Runner:
```
docker model run hf.co/inaciose/inacio-sft-d16v1
```

inacio-sft-d16v1

inacio-sft-d16v1 is the first instruction-tuned model of the nanochatpt project. It is obtained by supervised fine-tuning (SFT) of the base model inacio-d16v1, with the objective of improving conversational abilities while maintaining European Portuguese (PT-PT) as the primary language.

This release represents the first end-to-end demonstration of the complete nanochatpt training pipeline, from tokenizer training to pretraining and supervised fine-tuning.

Base Model

This model is fine-tuned from:

inaciose/inacio-d16v1

The base model was pretrained from scratch on a corpus primarily composed of European Portuguese text, together with a smaller proportion of English.

Model Architecture

Architecture: NanoChat
Decoder-only Transformer
Parameters: 872,416,058
Layers: 16
Hidden size: 1024
Attention heads: 8
KV heads: 8
Context length: 2048 tokens
Vocabulary: 65,536 tokens

Supervised Fine-Tuning Dataset

The instruction tuning stage uses NanoChat's TaskMixture framework.

Training data consists of:

CAIS MMLU (English)
OpenAI GSM8K (English)
inaciose/smol-smoltalk-pt-v1 (Mix European Portuguese 80%, English 20%)

The Portuguese conversational dataset smol-smoltalk-pt-v1 was created specifically for this project as a temporary PT-PT replacement for HuggingFaceTB/smol-smoltalk.

It is derived from:

AMALIA-LLM-0626-SFT-Dataset

Future releases will progressively replace the remaining English instruction datasets with Portuguese equivalents.

https://huggingface.co/datasets/cais/mmlu https://huggingface.co/datasets/openai/gsm8k https://huggingface.co/datasets/inaciose/smol-smoltalk-pt-v1

Training

Supervised fine-tuning was performed on the Modal cloud platform using:

4 × NVIDIA A100 SXM4 80GB

The model was initialized directly from the pretrained checkpoint of inacio-d16v1.

Intended Uses

This model is intended for:

conversational assistants
experimentation
educational purposes
research on European Portuguese language models

Current Capabilities

The model is capable of:

maintaining multi-turn conversations
following simple instructions
generating structured answers
responding primarily in Portuguese

Compared to the base model, it produces considerably more natural conversational responses and follows instruction formats learned during supervised fine-tuning.

Current Limitations

This is the first public SFT release of nanochatpt.

The model still presents significant limitations, including:

factual hallucinations
weak mathematical reasoning
inconsistent scientific explanations
occasional repetition
occasional generation of special conversation tokens (e.g. <|assistant_end|>)

These behaviours are expected for a first-generation instruction-tuned model trained on a relatively small Portuguese conversational corpus.

Future releases may improve both the instruction dataset and the amount of Portuguese pretraining data.

Requeriments

nanochatpt or nanochat cloned to your machine, install requirements. https://github.com/inaciose/nanochatpt

copy the model to your local folder copy the tokenizer to your local folder

cache/modal ├── chatsft_checkpoints │ └── d16 │ ├── meta_0XXXXX.json │ ├── model_0XXXXX.pt │ ├── optim_0XXXXX_rank0.pt │ ├── optim_0XXXXX_rank1.pt │ ├── optim_0XXXXX_rank2.pt │ └── optim_0XXXXX_rank3.pt └── tokenizer ├── token_bytes.pt └── tokenizer.pkl

export NANOCHAT_BASE_DIR=cache/modal

run the python -m scripts/chat_cli.py

Project

nanochatpt is an personal open research project whose objective is to train compact language models from scratch with European Portuguese as the primary language.

Future work includes:

larger pretraining corpora
improved Portuguese instruction datasets
Portuguese evaluation benchmarks
additional supervised fine-tuning
reinforcement learning alignment

Downloads last month: -; Downloads are not tracked for this model. How to track

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