README.md

metadata

language:
  - en
license: apache-2.0
library_name: transformers
tags:
  - code
  - moe
  - TensorBlock
  - GGUF
datasets:
  - andersonbcdefg/synthetic_retrieval_tasks
  - ise-uiuc/Magicoder-Evol-Instruct-110K
metrics:
  - code_eval
base_model: senseable/moe-x33
model-index:
  - name: moe-x33
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 26.19
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=senseable/moe-x33
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 26.44
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=senseable/moe-x33
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 24.93
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=senseable/moe-x33
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 51.14
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=senseable/moe-x33
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 50.99
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=senseable/moe-x33
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 0
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=senseable/moe-x33
          name: Open LLM Leaderboard

senseable/moe-x33 - GGUF

This repo contains GGUF format model files for senseable/moe-x33.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4242.

Our projects

Forge
An OpenAI-compatible multi-provider routing layer.
🚀 Try it now! 🚀
Awesome MCP Servers	TensorBlock Studio
A comprehensive collection of Model Context Protocol (MCP) servers.	A lightweight, open, and extensible multi-LLM interaction studio.
👀 See what we built 👀	👀 See what we built 👀

## Prompt template

Model file specification

Filename	Quant type	File Size	Description
moe-x33-Q2_K.gguf	Q2_K	21.625 GB	smallest, significant quality loss - not recommended for most purposes
moe-x33-Q3_K_S.gguf	Q3_K_S	25.425 GB	very small, high quality loss
moe-x33-Q3_K_M.gguf	Q3_K_M	28.280 GB	very small, high quality loss
moe-x33-Q3_K_L.gguf	Q3_K_L	30.763 GB	small, substantial quality loss
moe-x33-Q4_0.gguf	Q4_0	33.222 GB	legacy; small, very high quality loss - prefer using Q3_K_M
moe-x33-Q4_K_S.gguf	Q4_K_S	33.466 GB	small, greater quality loss
moe-x33-Q4_K_M.gguf	Q4_K_M	35.408 GB	medium, balanced quality - recommended
moe-x33-Q5_0.gguf	Q5_0	40.561 GB	legacy; medium, balanced quality - prefer using Q4_K_M
moe-x33-Q5_K_S.gguf	Q5_K_S	40.561 GB	large, low quality loss - recommended
moe-x33-Q5_K_M.gguf	Q5_K_M	41.687 GB	large, very low quality loss - recommended
moe-x33-Q6_K.gguf	Q6_K	48.358 GB	very large, extremely low quality loss
moe-x33-Q8_0	Q8_0	42.254 GB	very large, extremely low quality loss - not recommended

Downloading instruction

Command line

Firstly, install Huggingface Client

pip install -U "huggingface_hub[cli]"

Then, downoad the individual model file the a local directory

huggingface-cli download tensorblock/moe-x33-GGUF --include "moe-x33-Q2_K.gguf" --local-dir MY_LOCAL_DIR

If you wanna download multiple model files with a pattern (e.g., *Q4_K*gguf), you can try:

huggingface-cli download tensorblock/moe-x33-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'