Instructions to use matheusfarocha/linear_wjb_9k_subsample with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use matheusfarocha/linear_wjb_9k_subsample with Transformers:

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

pipe = pipeline("text-generation", model="matheusfarocha/linear_wjb_9k_subsample")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("matheusfarocha/linear_wjb_9k_subsample")
model = AutoModelForCausalLM.from_pretrained("matheusfarocha/linear_wjb_9k_subsample")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use matheusfarocha/linear_wjb_9k_subsample with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "matheusfarocha/linear_wjb_9k_subsample"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "matheusfarocha/linear_wjb_9k_subsample",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/matheusfarocha/linear_wjb_9k_subsample

SGLang

How to use matheusfarocha/linear_wjb_9k_subsample 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 "matheusfarocha/linear_wjb_9k_subsample" \
    --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": "matheusfarocha/linear_wjb_9k_subsample",
		"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 "matheusfarocha/linear_wjb_9k_subsample" \
        --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": "matheusfarocha/linear_wjb_9k_subsample",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use matheusfarocha/linear_wjb_9k_subsample with Docker Model Runner:
```
docker model run hf.co/matheusfarocha/linear_wjb_9k_subsample
```

linear

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

Merge Details

Merge Method

This model was merged using the Linear merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

dtype: bfloat16
merge_method: linear
modules:
  default:
    slices:
    - sources:
      - layer_range: [0, 26]
        model: aarnav11/gemma_1b_cares18k
        parameters:
          weight: 0.5
      - layer_range: [0, 26]
        model: matheusfarocha/gemini-3-1b-it-wildjailbreak-9k-subsample
        parameters:
          weight: 0.5

Downloads last month: 2

Safetensors

Model size

1.0B params

Tensor type

BF16

Model tree for matheusfarocha/linear_wjb_9k_subsample

aarnav11/gemma_1b_cares18k

matheusfarocha/gemini-3-1b-it-wildjailbreak-9k-subsample

Merge model

this model

Paper for matheusfarocha/linear_wjb_9k_subsample

Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time

Paper • 2203.05482 • Published Mar 10, 2022 • 9