Text Generation
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PyTorch
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Mixture of Experts
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music
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673_trillion_parameters
Instructions to use ZeppelinCorp/Charm_15 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZeppelinCorp/Charm_15 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ZeppelinCorp/Charm_15")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ZeppelinCorp/Charm_15") model = AutoModelForCausalLM.from_pretrained("ZeppelinCorp/Charm_15", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ZeppelinCorp/Charm_15 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ZeppelinCorp/Charm_15" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ZeppelinCorp/Charm_15", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ZeppelinCorp/Charm_15
- SGLang
How to use ZeppelinCorp/Charm_15 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 "ZeppelinCorp/Charm_15" \ --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": "ZeppelinCorp/Charm_15", "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 "ZeppelinCorp/Charm_15" \ --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": "ZeppelinCorp/Charm_15", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ZeppelinCorp/Charm_15 with Docker Model Runner:
docker model run hf.co/ZeppelinCorp/Charm_15
File size: 2,223 Bytes
6b81dd1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | import os
import json
import torch
import datasets
from torch.utils.data import DataLoader, Dataset
from transformers import PreTrainedTokenizerFast
class CustomDataset(Dataset):
def __init__(self, data, tokenizer, max_length=512):
self.data = data
self.tokenizer = tokenizer
self.max_length = max_length
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
text = self.data[idx]["text"]
inputs = self.tokenizer(
text,
max_length=self.max_length,
padding="max_length",
truncation=True,
return_tensors="pt"
)
return {
"input_ids": inputs["input_ids"].squeeze(0),
"attention_mask": inputs["attention_mask"].squeeze(0)
}
class DataLoaderHandler:
def __init__(self, dataset_path, tokenizer_path, batch_size=8, max_length=512):
self.dataset_path = dataset_path
self.tokenizer = PreTrainedTokenizerFast(tokenizer_file=tokenizer_path)
self.batch_size = batch_size
self.max_length = max_length
def load_dataset(self):
if self.dataset_path.endswith(".json"):
with open(self.dataset_path, "r", encoding="utf-8") as f:
data = json.load(f)
elif self.dataset_path.endswith(".jsonl"):
data = [json.loads(line) for line in open(self.dataset_path, "r", encoding="utf-8")]
else:
raise ValueError("Unsupported dataset format. Use JSON or JSONL.")
return data
def get_dataloader(self):
data = self.load_dataset()
dataset = CustomDataset(data, self.tokenizer, self.max_length)
return DataLoader(dataset, batch_size=self.batch_size, shuffle=True)
if __name__ == "__main__":
dataset_path = "data/dataset.jsonl" # Update with actual dataset path
tokenizer_path = "tokenizer.json" # Update with actual tokenizer path
batch_size = 16
data_loader_handler = DataLoaderHandler(dataset_path, tokenizer_path, batch_size)
dataloader = data_loader_handler.get_dataloader()
for batch in dataloader:
print(batch["input_ids"].shape, batch["attention_mask"].shape)
break |