Text Generation
Transformers
mixtral
Mixture of Experts
frankenmoe
Merge
mergekit
lazymergekit
lxuechen/phi-2-sft
mrm8488/phi-2-coder
Walmart-the-bag/phi-2-uncensored
ArtifactAI/phi-2-arxiv-physics-instruct
custom_code
Instructions to use ssands1979/FrankenPhi2-4x with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ssands1979/FrankenPhi2-4x with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ssands1979/FrankenPhi2-4x", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ssands1979/FrankenPhi2-4x", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("ssands1979/FrankenPhi2-4x", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ssands1979/FrankenPhi2-4x with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ssands1979/FrankenPhi2-4x" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ssands1979/FrankenPhi2-4x", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ssands1979/FrankenPhi2-4x
- SGLang
How to use ssands1979/FrankenPhi2-4x 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 "ssands1979/FrankenPhi2-4x" \ --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": "ssands1979/FrankenPhi2-4x", "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 "ssands1979/FrankenPhi2-4x" \ --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": "ssands1979/FrankenPhi2-4x", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ssands1979/FrankenPhi2-4x with Docker Model Runner:
docker model run hf.co/ssands1979/FrankenPhi2-4x
FrankenPhi2-4x
FrankenPhi2-4x is a Mixure of Experts (MoE) made with the following models using LazyMergekit:
- lxuechen/phi-2-sft
- mrm8488/phi-2-coder
- Walmart-the-bag/phi-2-uncensored
- ArtifactAI/phi-2-arxiv-physics-instruct
π§© Configuration
base_model: microsoft/phi-2
experts:
- source_model: lxuechen/phi-2-sft
positive_prompts:
- "chat"
- "assistant"
- "tell me"
- "explain"
- source_model: mrm8488/phi-2-coder
positive_prompts:
- "code"
- "python"
- "javascript"
- "programming"
- "algorithm"
- source_model: Walmart-the-bag/phi-2-uncensored
positive_prompts:
- "storywriting"
- "write"
- "scene"
- "story"
- "character"
- source_model: ArtifactAI/phi-2-arxiv-physics-instruct
positive_prompts:
- "physics"
- "math"
- "mathematics"
- "solve"
- "count"
π» Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "ssands1979/FrankenPhi2-4x"
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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