Instructions to use AIInstituteLNCC/manaca-1b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIInstituteLNCC/manaca-1b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="AIInstituteLNCC/manaca-1b-instruct")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AIInstituteLNCC/manaca-1b-instruct")
model = AutoModelForCausalLM.from_pretrained("AIInstituteLNCC/manaca-1b-instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AIInstituteLNCC/manaca-1b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIInstituteLNCC/manaca-1b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIInstituteLNCC/manaca-1b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/AIInstituteLNCC/manaca-1b-instruct

SGLang

How to use AIInstituteLNCC/manaca-1b-instruct 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 "AIInstituteLNCC/manaca-1b-instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIInstituteLNCC/manaca-1b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "AIInstituteLNCC/manaca-1b-instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIInstituteLNCC/manaca-1b-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AIInstituteLNCC/manaca-1b-instruct with Docker Model Runner:
```
docker model run hf.co/AIInstituteLNCC/manaca-1b-instruct
```

menezesbruno commited on Sep 4

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@@ -24,7 +24,7 @@ datasets:
 [![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)
 [![Base model](https://img.shields.io/badge/%F0%9F%A4%97%20Base-menezesbruno%2Fmanaca--1b--base-yellow.svg)](https://huggingface.co/menezesbruno/manaca-1b-base)
-[![Code & reproducibility](https://img.shields.io/badge/GitHub-manaca--1b--instruct-181717.svg)](https://github.com/brunoleomenezes/manaca-1b-instruct)
 [![Language: PT-BR](https://img.shields.io/badge/Language-PT--BR-009c3b.svg)]()
 [![Institution: LNCC](https://img.shields.io/badge/Institution-LNCC-002776.svg)](https://www.lncc.br)

 [![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)
 [![Base model](https://img.shields.io/badge/%F0%9F%A4%97%20Base-menezesbruno%2Fmanaca--1b--base-yellow.svg)](https://huggingface.co/menezesbruno/manaca-1b-base)
+[![Code & reproducibility](https://img.shields.io/badge/GitHub-manaca--1b--instruct-181717.svg)](https://github.com/Instituto-IA-LNCC/manaca-1b-instruct/)
 [![Language: PT-BR](https://img.shields.io/badge/Language-PT--BR-009c3b.svg)]()
 [![Institution: LNCC](https://img.shields.io/badge/Institution-LNCC-002776.svg)](https://www.lncc.br)