Text Generation
Transformers
Safetensors
mixtral
Mixture of Experts
frankenmoe
Merge
mergekit
lazymergekit
Corianas/Microllama_Char_88k_step
text-generation-inference
Instructions to use Corianas/microchar_moe with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Corianas/microchar_moe with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Corianas/microchar_moe")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Corianas/microchar_moe") model = AutoModelForCausalLM.from_pretrained("Corianas/microchar_moe") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Corianas/microchar_moe with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Corianas/microchar_moe" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Corianas/microchar_moe", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Corianas/microchar_moe
- SGLang
How to use Corianas/microchar_moe 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 "Corianas/microchar_moe" \ --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": "Corianas/microchar_moe", "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 "Corianas/microchar_moe" \ --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": "Corianas/microchar_moe", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Corianas/microchar_moe with Docker Model Runner:
docker model run hf.co/Corianas/microchar_moe
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Corianas/microchar_moe")
model = AutoModelForCausalLM.from_pretrained("Corianas/microchar_moe")Quick Links
microchar_moe
microchar_moe is a Mixture of Experts (MoE) made with the following models using LazyMergekit:
π§© Configuration
base_model: Corianas/Microllama_Char_88k_step
gate_mode: random # one of "hidden", "cheap_embed", or "random"
dtype: bfloat16 # output dtype (float32, float16, or bfloat16)
## (optional)
# experts_per_token: 2
experts:
- source_model: Corianas/Microllama_Char_88k_step
positive_prompts:
- ""
## (optional)
# negative_prompts:
# - "This is a prompt expert_model_1 should not be used for"
- source_model: Corianas/Microllama_Char_88k_step
positive_prompts:
- ""
π» Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Corianas/microchar_moe"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
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Model tree for Corianas/microchar_moe
Base model
Corianas/Microllama_Char_88k_step
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Corianas/microchar_moe")