Text Generation
Transformers
Safetensors
llama
llama2
fused
cpu
context-8000
fusion-all2one
tensor-fusion
bias-removal
decode
coherence-enhancement
custom-code
text-generation-inference
Instructions to use jnjj/xddd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jnjj/xddd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jnjj/xddd")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jnjj/xddd") model = AutoModelForCausalLM.from_pretrained("jnjj/xddd") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use jnjj/xddd with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jnjj/xddd" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jnjj/xddd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jnjj/xddd
- SGLang
How to use jnjj/xddd 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 "jnjj/xddd" \ --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": "jnjj/xddd", "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 "jnjj/xddd" \ --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": "jnjj/xddd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jnjj/xddd with Docker Model Runner:
docker model run hf.co/jnjj/xddd
xddd
Este repositorio incluye:
hghghgkskdmskdms/xdddcon fusi贸n completa de layers en 1 (sin eliminaci贸n de originales)- Fusi贸n de todos los tensores en un 煤nico vector continuo
- Eliminaci贸n de bias y desactivaci贸n de censura
- Configuraci贸n de generaci贸n: do_sample=True, temp=0.7, top_p=0.9, repetition_penalty=1.2, no_repeat_ngram_size=3
- Funciones de decodificaci贸n de tokens, par谩metros, respuestas, layers, neuronas, tensores, arquitectura y tensor fusionado
- max_position_embeddings: 8000
- torch_dtype: float32
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("jnjj/xddd", torch_dtype="float32", trust_remote_code=True)
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