Instructions to use grimjim/Llama-3-Steerpike-v1-OAS-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/Llama-3-Steerpike-v1-OAS-8B with Transformers:

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

pipe = pipeline("text-generation", model="grimjim/Llama-3-Steerpike-v1-OAS-8B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("grimjim/Llama-3-Steerpike-v1-OAS-8B")
model = AutoModelForMultimodalLM.from_pretrained("grimjim/Llama-3-Steerpike-v1-OAS-8B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use grimjim/Llama-3-Steerpike-v1-OAS-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "grimjim/Llama-3-Steerpike-v1-OAS-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/Llama-3-Steerpike-v1-OAS-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/grimjim/Llama-3-Steerpike-v1-OAS-8B

SGLang

How to use grimjim/Llama-3-Steerpike-v1-OAS-8B 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 "grimjim/Llama-3-Steerpike-v1-OAS-8B" \
    --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": "grimjim/Llama-3-Steerpike-v1-OAS-8B",
		"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 "grimjim/Llama-3-Steerpike-v1-OAS-8B" \
        --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": "grimjim/Llama-3-Steerpike-v1-OAS-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use grimjim/Llama-3-Steerpike-v1-OAS-8B with Docker Model Runner:
```
docker model run hf.co/grimjim/Llama-3-Steerpike-v1-OAS-8B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Llama-3-Steerpike-v1-OAS-8B

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

This model might result in characters who are "too" smart if conversation veers into the analytical, but that may be fine depending on the context. There are issues early on with the consistency of formatting, though that will stabilize with more context. This model is imperfect, but interesting.

Tested lightly with Instruct prompts, minP=0.01, and temperature 1+.

Built with Meta Llama 3.

Merge Details

Merge Method

This model was merged using the task arithmetic merge method using mlabonne/NeuralDaredevil-8B-abliterated as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: mlabonne/NeuralDaredevil-8B-abliterated
dtype: bfloat16
merge_method: task_arithmetic
slices:
- sources:
  - layer_range: [0, 32]
    model: mlabonne/NeuralDaredevil-8B-abliterated
  - layer_range: [0, 32]
    model: NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS
    parameters:
      weight: 0.5
  - layer_range: [0, 32]
    model: Hastagaras/Halu-OAS-8B-Llama3
    parameters:
      weight: 0.2
  - layer_range: [0, 32]
    model: openlynn/Llama-3-Soliloquy-8B-v2
    parameters:
      weight: 0.03
  - layer_range: [0, 32]
    model: grimjim/llama-3-aaditya-OpenBioLLM-8B
    parameters:
      weight: 0.1