Instructions to use Jksaw/Pxled with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jksaw/Pxled with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Jksaw/Pxled")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Jksaw/Pxled", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Jksaw/Pxled with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Jksaw/Pxled" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Jksaw/Pxled", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Jksaw/Pxled
- SGLang
How to use Jksaw/Pxled 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 "Jksaw/Pxled" \ --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": "Jksaw/Pxled", "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 "Jksaw/Pxled" \ --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": "Jksaw/Pxled", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Jksaw/Pxled with Docker Model Runner:
docker model run hf.co/Jksaw/Pxled
metadata
license: apache-2.0
datasets:
- HuggingFaceH4/ultrachat_200k
language:
- en
base_model:
- meta-llama/Meta-Llama-3-8B
pipeline_tag: text-generation
tags:
- text-generation
- transformers
- pytorch
- instruct
- chat
- llama
- apache-2.0
metrics:
- mauve
new_version: hesamation/Qwen3.6-35B-A3B-Claude-4.6-Opus-Reasoning-Distilled-GGUF
Pxled
A fine-tuned version of Meta-Llama-3-8B trained on the Ultrachat dataset.
Model Details
- Base Model: meta-llama/Meta-Llama-3-8B
- Dataset: HuggingFaceH4/ultrachat_200k
- License: Apache 2.0
How to Use
from transformers import pipeline
pipe = pipeline(
"text-generation",
model="Jksaw/Pxled",
device="cuda" # remove if no GPU
)
print(pipe("Hello, how are you?", max_new_tokens=256)[0]['generated_text'])