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", device_map="auto") - 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
Create README.md
Browse files
README.md
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---
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base_model:
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- Open-Orca/Mistral-7B-OpenOrca
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- akjindal53244/Mistral-7B-v0.1-Open-Platypus
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- WizardLM/WizardMath-7B-V1.1
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- mistralai/Mistral-7B-v0.1
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library_name: transformers
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tags:
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- mergekit
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- merge
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---
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# models
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This is a merge of pre-trained language models created using a fork of [mergekit](https://github.com/cg123/mergekit).
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## Merge Details
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### Merge Method
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This model was merged using an experimental merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base.
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### Models Merged
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The following models were included in the merge:
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* [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)
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* [akjindal53244/Mistral-7B-v0.1-Open-Platypus](https://huggingface.co/akjindal53244/Mistral-7B-v0.1-Open-Platypus)
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* [WizardLM/WizardMath-7B-V1.1](https://huggingface.co/WizardLM/WizardMath-7B-V1.1)
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### Configuration
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The following YAML configuration was used to produce this model:
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```yaml
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models:
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- model: Open-Orca/Mistral-7B-OpenOrca
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parameters:
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density: [1, 0.7, 0.1] # density gradient
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weight: 1.0
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- model: akjindal53244/Mistral-7B-v0.1-Open-Platypus
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parameters:
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density: 0.5
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weight: [0, 0.3, 0.7, 1] # weight gradient
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- model: WizardLM/WizardMath-7B-V1.1
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parameters:
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density: 0.33
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weight:
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- filter: mlp
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value: 0.5
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- value: 0
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merge_method: sample_ties
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base_model: mistralai/Mistral-7B-v0.1
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parameters:
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normalize: true
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int8_mask: true
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dtype: float16
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```
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