Instructions to use Azazelle/Moko-SAMPLE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Azazelle/Moko-SAMPLE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Azazelle/Moko-SAMPLE")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Azazelle/Moko-SAMPLE") model = AutoModelForCausalLM.from_pretrained("Azazelle/Moko-SAMPLE") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Azazelle/Moko-SAMPLE with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Azazelle/Moko-SAMPLE" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Azazelle/Moko-SAMPLE", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Azazelle/Moko-SAMPLE
- SGLang
How to use Azazelle/Moko-SAMPLE 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 "Azazelle/Moko-SAMPLE" \ --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": "Azazelle/Moko-SAMPLE", "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 "Azazelle/Moko-SAMPLE" \ --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": "Azazelle/Moko-SAMPLE", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Azazelle/Moko-SAMPLE with Docker Model Runner:
docker model run hf.co/Azazelle/Moko-SAMPLE
| pipeline_tag: text-generation | |
| base_model: | |
| - mistralai/Mistral-7B-v0.1 | |
| - WizardLM/WizardMath-7B-V1.1 | |
| - akjindal53244/Mistral-7B-v0.1-Open-Platypus | |
| - Open-Orca/Mistral-7B-OpenOrca | |
| library_name: transformers | |
| tags: | |
| - mergekit | |
| - merge | |
| license: cc-by-4.0 | |
| # Moko-Sample | |
| This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). | |
| ## Merge Details | |
| ### Merge Method | |
| This model was merged using the sample_ties merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base. | |
| ### Models Merged | |
| The following models were included in the merge: | |
| * [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1) | |
| * [akjindal53244/Mistral-7B-v0.1-Open-Platypus](https://huggingface.co/akjindal53244/Mistral-7B-v0.1-Open-Platypus) | |
| * [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca) | |
| ### Configuration | |
| The following YAML configuration was used to produce this model: | |
| ```yaml | |
| models: | |
| - model: Open-Orca/Mistral-7B-OpenOrca | |
| parameters: | |
| density: [1, 0.7, 0.1] # density gradient | |
| weight: 1.0 | |
| - model: akjindal53244/Mistral-7B-v0.1-Open-Platypus | |
| parameters: | |
| density: 0.5 | |
| weight: [0, 0.3, 0.7, 1] # weight gradient | |
| - model: WizardLM/WizardMath-7B-V1.1 | |
| parameters: | |
| density: 0.33 | |
| weight: | |
| - filter: mlp | |
| value: 0.5 | |
| - value: 0 | |
| merge_method: sample_ties | |
| base_model: mistralai/Mistral-7B-v0.1 | |
| parameters: | |
| normalize: true | |
| int8_mask: true | |
| dtype: float16 | |
| ``` | |