Instructions to use Elfrino/GoldenGidget-20B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Elfrino/GoldenGidget-20B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Elfrino/GoldenGidget-20B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Elfrino/GoldenGidget-20B") model = AutoModelForCausalLM.from_pretrained("Elfrino/GoldenGidget-20B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Elfrino/GoldenGidget-20B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Elfrino/GoldenGidget-20B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Elfrino/GoldenGidget-20B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Elfrino/GoldenGidget-20B
- SGLang
How to use Elfrino/GoldenGidget-20B 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 "Elfrino/GoldenGidget-20B" \ --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": "Elfrino/GoldenGidget-20B", "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 "Elfrino/GoldenGidget-20B" \ --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": "Elfrino/GoldenGidget-20B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Elfrino/GoldenGidget-20B with Docker Model Runner:
docker model run hf.co/Elfrino/GoldenGidget-20B
merge
Interesting results, still testing.
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the SLERP merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
merge_method: slerp
dtype: bfloat16
slices:
- sources:
- model: Undi95/PsyMedRP-v1-20B
layer_range: [0, 62]
- model: Undi95/MXLewd-L2-20B
layer_range: [0, 62]
base_model: Undi95/MXLewd-L2-20B
parameters:
t:
- 0.5
- 0.72
- 0.69
- 0.47
- 0.28
- 0.31
- 0.53
- 0.72
- 0.68
- 0.45
- 0.28
- 0.33
- 0.56
- 0.72
- 0.66
- 0.43
- 0.27
- 0.34
- 0.59
- 0.71
- 0.64
- 0.41
- 0.27
- 0.36
- 0.62
- 0.70
- 0.61
- 0.39
- 0.27
- 0.38
- 0.65
- 0.68
- 0.59
- 0.37
- 0.27
- 0.41
- 0.67
- 0.66
- 0.56
- 0.35
- 0.27
- 0.44
- 0.69
- 0.63
- 0.54
- 0.33
- 0.28
- 0.47
- 0.70
- 0.60
- 0.51
- 0.31
- 0.29
- 0.50
- 0.71
- 0.57
- 0.49
- 0.30
- 0.30
- 0.53
- 0.71
- 0.54
- 0.46
- 0.29
- 0.31
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