README.md

metadata

language:
  - en
license: mit
tags:
  - moe
  - TensorBlock
  - GGUF
pipeline_tag: text-generation
base_model: TomGrc/FusionNet_34Bx2_MoE
model-index:
  - name: FusionNet_34Bx2_MoE
    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: 72.95
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_34Bx2_MoE
          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: 86.22
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_34Bx2_MoE
          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: 77.05
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_34Bx2_MoE
          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: 71.31
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_34Bx2_MoE
          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: 83.98
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_34Bx2_MoE
          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: 70.89
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_34Bx2_MoE
          name: Open LLM Leaderboard

Feedback and support: TensorBlock's Twitter/X, Telegram Group and Discord server

TomGrc/FusionNet_34Bx2_MoE - GGUF

This repo contains GGUF format model files for TomGrc/FusionNet_34Bx2_MoE.

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

Run them on the TensorBlock client using your local machine ↗

Prompt template

[INST] <<SYS>>
{system_prompt}
<</SYS>>

{prompt} [/INST]

Model file specification

Filename	Quant type	File Size	Description
FusionNet_34Bx2_MoE-Q2_K.gguf	Q2_K	22.394 GB	smallest, significant quality loss - not recommended for most purposes
FusionNet_34Bx2_MoE-Q3_K_S.gguf	Q3_K_S	26.318 GB	very small, high quality loss
FusionNet_34Bx2_MoE-Q3_K_M.gguf	Q3_K_M	29.237 GB	very small, high quality loss
FusionNet_34Bx2_MoE-Q3_K_L.gguf	Q3_K_L	31.768 GB	small, substantial quality loss
FusionNet_34Bx2_MoE-Q4_0.gguf	Q4_0	34.334 GB	legacy; small, very high quality loss - prefer using Q3_K_M
FusionNet_34Bx2_MoE-Q4_K_S.gguf	Q4_K_S	34.594 GB	small, greater quality loss
FusionNet_34Bx2_MoE-Q4_K_M.gguf	Q4_K_M	36.661 GB	medium, balanced quality - recommended
FusionNet_34Bx2_MoE-Q5_0.gguf	Q5_0	41.878 GB	legacy; medium, balanced quality - prefer using Q4_K_M
FusionNet_34Bx2_MoE-Q5_K_S.gguf	Q5_K_S	41.878 GB	large, low quality loss - recommended
FusionNet_34Bx2_MoE-Q5_K_M.gguf	Q5_K_M	43.077 GB	large, very low quality loss - recommended
FusionNet_34Bx2_MoE-Q6_K.gguf	Q6_K	49.893 GB	very large, extremely low quality loss
FusionNet_34Bx2_MoE-Q8_0	Q8_0	64.621 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/FusionNet_34Bx2_MoE-GGUF --include "FusionNet_34Bx2_MoE-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/FusionNet_34Bx2_MoE-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'