Instructions to use TheBloke/wizard-mega-13B-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/wizard-mega-13B-GPTQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheBloke/wizard-mega-13B-GPTQ")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TheBloke/wizard-mega-13B-GPTQ") model = AutoModelForCausalLM.from_pretrained("TheBloke/wizard-mega-13B-GPTQ") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TheBloke/wizard-mega-13B-GPTQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheBloke/wizard-mega-13B-GPTQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TheBloke/wizard-mega-13B-GPTQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TheBloke/wizard-mega-13B-GPTQ
- SGLang
How to use TheBloke/wizard-mega-13B-GPTQ 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 "TheBloke/wizard-mega-13B-GPTQ" \ --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": "TheBloke/wizard-mega-13B-GPTQ", "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 "TheBloke/wizard-mega-13B-GPTQ" \ --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": "TheBloke/wizard-mega-13B-GPTQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TheBloke/wizard-mega-13B-GPTQ with Docker Model Runner:
docker model run hf.co/TheBloke/wizard-mega-13B-GPTQ
Using the model with Langchain agents sometimes breaks because the special end of sequence token "</s>" is added at the end of the output
I was playing the model and langchain agent. I noticed after some steps the model adds the special token "< /s>" at the end of the output and makes the agent breaks. The root. I am not an expert so that I did get the reason why the model adds it. Anyway, I added a simple parsing rule to remove this string from the output and now it works pretty well. I just share this if someone in the community was struggling with this model when combined with langchain agent.
Here is a sample example (I added a white space in the token otherwise it does not displays)
Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
Instruction:
Write a poem about a squirrel who like soccer
Response: < /s>
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
namespace _031_MinimumNumberOfOperationsToReachTargetValue
{
class Program
{
static void Main(string[] args)
{
int n = int.Parse(Console.ReadLine());
int target = int.Parse(Console.ReadLine()) - (n / 2);
Console.WriteLine("The minimum number of operations to reach " + target + " from " + n + " is " + GetMinOperations(target, n));
}
private static int GetMinOperations(int target, int n)
{
if (n == 0 || target < 0) return 0;
List<int> list
forget my dump remarks above. The issue was first my lack of knowledge on how it works and second a misconfiguration of the text generation webui API.
OK, glad you got it sorted