Text Generation
Transformers
Safetensors
mixtral
Mixture of Experts
frankenmoe
Merge
mergekit
lazymergekit
LLM360/AmberChat
wannaphong/han-llm-7b-v2
text-generation-inference
Instructions to use Manichik/HanAmber-7b-MOE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Manichik/HanAmber-7b-MOE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Manichik/HanAmber-7b-MOE")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Manichik/HanAmber-7b-MOE") model = AutoModelForCausalLM.from_pretrained("Manichik/HanAmber-7b-MOE") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Manichik/HanAmber-7b-MOE with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Manichik/HanAmber-7b-MOE" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Manichik/HanAmber-7b-MOE", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Manichik/HanAmber-7b-MOE
- SGLang
How to use Manichik/HanAmber-7b-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 "Manichik/HanAmber-7b-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": "Manichik/HanAmber-7b-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 "Manichik/HanAmber-7b-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": "Manichik/HanAmber-7b-MOE", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Manichik/HanAmber-7b-MOE with Docker Model Runner:
docker model run hf.co/Manichik/HanAmber-7b-MOE
HanAmber-7b-MOE
HanAmber-7b-MOE is a Mixure of Experts (MoE) made with the following models using LazyMergekit:
🧩 Configuration
base_model: LLM360/AmberChat
dtype: float16
gate_mode: cheap_embed
experts:
- source_model: LLM360/AmberChat
positive_prompts: ["You are an helpful as general-pupose assistant."]
- source_model: wannaphong/han-llm-7b-v2
positive_prompts:
- "คุณช่วยฉันหน่อยได้ไหม"
- "คุณช่วยแปลประโยคนี้เป็นภาษาไทยได้ไหม"
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Manichik/HanAmber-7b-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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