Text Generation
Transformers
Safetensors
mixtral
Merge
mergekit
Mixture of Experts
frankenmoe
abacusai/Llama-3-Smaug-8B
cognitivecomputations/dolphin-2.9-llama3-8b
Weyaxi/Einstein-v6.1-Llama3-8B
dreamgen-preview/opus-v1.2-llama-3-8b-base-run3.4-epoch2
text-generation-inference
Instructions to use saucam/Skyro-4X8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use saucam/Skyro-4X8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="saucam/Skyro-4X8B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("saucam/Skyro-4X8B") model = AutoModelForCausalLM.from_pretrained("saucam/Skyro-4X8B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use saucam/Skyro-4X8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "saucam/Skyro-4X8B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "saucam/Skyro-4X8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/saucam/Skyro-4X8B
- SGLang
How to use saucam/Skyro-4X8B 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 "saucam/Skyro-4X8B" \ --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": "saucam/Skyro-4X8B", "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 "saucam/Skyro-4X8B" \ --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": "saucam/Skyro-4X8B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use saucam/Skyro-4X8B with Docker Model Runner:
docker model run hf.co/saucam/Skyro-4X8B
| name: "Skyro-4X8B" | |
| base_model: meta-llama/Meta-Llama-3-8B | |
| gate_mode: hidden | |
| experts: | |
| - source_model: abacusai/Llama-3-Smaug-8B | |
| positive_prompts: | |
| - "chat" | |
| - "assistant" | |
| - "tell me" | |
| - "explain" | |
| - "I want" | |
| - source_model: cognitivecomputations/dolphin-2.9-llama3-8b | |
| positive_prompts: | |
| - "math" | |
| - "mathematics" | |
| - "code" | |
| - "engineering" | |
| - "solve" | |
| - "logic" | |
| - "rationality" | |
| - "puzzle" | |
| - "solve" | |
| - source_model: Weyaxi/Einstein-v6.1-Llama3-8B | |
| positive_prompts: | |
| - "science" | |
| - "medical" | |
| - "physics" | |
| - "engineering" | |
| - "math" | |
| - "logic" | |
| - "rationality" | |
| - "mathematics" | |
| - "solve" | |
| - source_model: dreamgen-preview/opus-v1.2-llama-3-8b-base-run3.4-epoch2 | |
| positive_prompts: | |
| - "story" | |
| - "roleplay" | |
| - "role-play" | |
| - "storywriting" | |
| - "character" | |
| - "narrative" | |
| - "creative" |