Instructions to use bmi-labmedinfo/Igea-1B-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bmi-labmedinfo/Igea-1B-instruct with Transformers:

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

pipe = pipeline("text-generation", model="bmi-labmedinfo/Igea-1B-instruct")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("bmi-labmedinfo/Igea-1B-instruct")
model = AutoModelForCausalLM.from_pretrained("bmi-labmedinfo/Igea-1B-instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bmi-labmedinfo/Igea-1B-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bmi-labmedinfo/Igea-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": "bmi-labmedinfo/Igea-1B-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bmi-labmedinfo/Igea-1B-instruct

SGLang

How to use bmi-labmedinfo/Igea-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 "bmi-labmedinfo/Igea-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": "bmi-labmedinfo/Igea-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 "bmi-labmedinfo/Igea-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": "bmi-labmedinfo/Igea-1B-instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use bmi-labmedinfo/Igea-1B-instruct with Docker Model Runner:
```
docker model run hf.co/bmi-labmedinfo/Igea-1B-instruct
```

Acknowledge terms and conditions to accept the repository

Our team may take 2-3 days to process your request

This is a pretrained model that should be fine-tuned to perform downstream tasks. You agree to not use the model to conduct experiments that cause harm to human subjects, or to perform any medical-related task.

Prompt

"""
{System prompt here}

### Istruzione:
{Instruction here.}

### Risposta:
{Answer here. Leave blank for inference}
"""

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Safetensors

Model size

1B params

Tensor type

BF16

Model tree for bmi-labmedinfo/Igea-1B-instruct

Base model

sapienzanlp/Minerva-1B-base-v1.0

Finetuned

bmi-labmedinfo/Igea-1B-v0.1

Finetuned

(1)

this model

Space using bmi-labmedinfo/Igea-1B-instruct 1

Collection including bmi-labmedinfo/Igea-1B-instruct

Igea Models

Collection

6 items • Updated Feb 3, 2025 • 3