README.md · tensorblock/Orca-2.0-Tau-1.8B-GGUF at main

metadata

language:
  - en
license: other
library_name: transformers
datasets:
  - Open-Orca/SlimOrca
  - m-a-p/Code-Feedback
  - MaziyarPanahi/WizardLM_evol_instruct_V2_196k
  - camel-ai/math
  - camel-ai/physics
  - camel-ai/biology
  - camel-ai/chemistry
  - LDJnr/Capybara
  - jondurbin/airoboros-3.2
  - microsoft/orca-math-word-problems-200k
inference:
  parameters:
    do_sample: true
    temperature: 0.8
    top_p: 0.95
    top_k: 40
    max_new_tokens: 250
    repetition_penalty: 1.1
base_model: M4-ai/Orca-2.0-Tau-1.8B
tags:
  - TensorBlock
  - GGUF
model-index:
  - name: Orca-2.0-Tau-1.8B
    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: 37.12
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B
          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: 61.13
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B
          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: 45.27
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B
          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: 39.1
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B
          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: 59.59
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B
          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: 28.96
            name: accuracy
        source:
          url: >-
            https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard?query=M4-ai/Orca-2.0-Tau-1.8B
          name: Open LLM Leaderboard

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M4-ai/Orca-2.0-Tau-1.8B - GGUF

This repo contains GGUF format model files for M4-ai/Orca-2.0-Tau-1.8B.

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

<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

Model file specification

Filename	Quant type	File Size	Description
Orca-2.0-Tau-1.8B-Q2_K.gguf	Q2_K	0.847 GB	smallest, significant quality loss - not recommended for most purposes
Orca-2.0-Tau-1.8B-Q3_K_S.gguf	Q3_K_S	0.954 GB	very small, high quality loss
Orca-2.0-Tau-1.8B-Q3_K_M.gguf	Q3_K_M	1.016 GB	very small, high quality loss
Orca-2.0-Tau-1.8B-Q3_K_L.gguf	Q3_K_L	1.056 GB	small, substantial quality loss
Orca-2.0-Tau-1.8B-Q4_0.gguf	Q4_0	1.120 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Orca-2.0-Tau-1.8B-Q4_K_S.gguf	Q4_K_S	1.158 GB	small, greater quality loss
Orca-2.0-Tau-1.8B-Q4_K_M.gguf	Q4_K_M	1.218 GB	medium, balanced quality - recommended
Orca-2.0-Tau-1.8B-Q5_0.gguf	Q5_0	1.311 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Orca-2.0-Tau-1.8B-Q5_K_S.gguf	Q5_K_S	1.328 GB	large, low quality loss - recommended
Orca-2.0-Tau-1.8B-Q5_K_M.gguf	Q5_K_M	1.377 GB	large, very low quality loss - recommended
Orca-2.0-Tau-1.8B-Q6_K.gguf	Q6_K	1.579 GB	very large, extremely low quality loss
Orca-2.0-Tau-1.8B-Q8_0.gguf	Q8_0	1.958 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/Orca-2.0-Tau-1.8B-GGUF --include "Orca-2.0-Tau-1.8B-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/Orca-2.0-Tau-1.8B-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'