Instructions to use jeiku/Gattaca_3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jeiku/Gattaca_3B with Transformers:

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

pipe = pipeline("text-generation", model="jeiku/Gattaca_3B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("jeiku/Gattaca_3B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jeiku/Gattaca_3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jeiku/Gattaca_3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jeiku/Gattaca_3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jeiku/Gattaca_3B

SGLang

How to use jeiku/Gattaca_3B 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 "jeiku/Gattaca_3B" \
    --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": "jeiku/Gattaca_3B",
		"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 "jeiku/Gattaca_3B" \
        --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": "jeiku/Gattaca_3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jeiku/Gattaca_3B with Docker Model Runner:
```
docker model run hf.co/jeiku/Gattaca_3B
```

Mixed

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using jeiku/RocketHermesZephyrBoros_3B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: jeiku/RocketHermesZephyrBoros_3B+jeiku/Bluemoon_cleaned_StableLM
    parameters:
      weight: 0.30
      density: 0.25
  - model: jeiku/RocketHermesZephyrBoros_3B+jeiku/Toxic_DPO_StableLM
    parameters:
      weight: 0.25
      density: 0.25
  - model: jeiku/RocketHermesZephyrBoros_3B+jeiku/Theory_of_Mind_RP_128_StableLM
    parameters:
      weight: 0.35
      density: 0.25
  - model: jeiku/RocketHermesZephyrBoros_3B+jeiku/No_Robots_Alpaca_StableLM
    parameters:
      weight: 0.25
      density: 0.25
  - model: jeiku/RocketHermesZephyrBoros_3B+jeiku/Everything_v3_128_StableLM
    parameters:
      weight: 0.5
      density: 0.5
  - model: jeiku/RocketHermesZephyrBoros_3B+jeiku/Gnosis_StableLM
    parameters:
      weight: 0.4
      density: 0.4
  - model: jeiku/RocketHermesZephyrBoros_3B+jeiku/Erotica_StableLM
    parameters:
      weight: 0.20
      density: 0.20
merge_method: dare_ties
base_model: jeiku/RocketHermesZephyrBoros_3B
parameters:
dtype: bfloat16