README.md

metadata

language:
  - en
license: apache-2.0
tags:
  - gpt2
  - dpo
  - trl
  - TensorBlock
  - GGUF
datasets:
  - HuggingFaceH4/ultrachat_200k
  - Intel/orca_dpo_pairs
pipeline_tag: text-generation
base_model: Sharathhebbar24/chat_gpt2_dpo
model-index:
  - name: chat_gpt2_dpo
    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: 23.98
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2_dpo
          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: 31.22
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2_dpo
          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: 24.95
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2_dpo
          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: 41.26
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2_dpo
          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: 49.96
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/chat_gpt2_dpo
          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=Sharathhebbar24/chat_gpt2_dpo
          name: Open LLM Leaderboard

Sharathhebbar24/chat_gpt2_dpo - GGUF

This repo contains GGUF format model files for Sharathhebbar24/chat_gpt2_dpo.

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

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## Prompt template

Model file specification

Filename	Quant type	File Size	Description
chat_gpt2_dpo-Q2_K.gguf	Q2_K	0.081 GB	smallest, significant quality loss - not recommended for most purposes
chat_gpt2_dpo-Q3_K_S.gguf	Q3_K_S	0.090 GB	very small, high quality loss
chat_gpt2_dpo-Q3_K_M.gguf	Q3_K_M	0.098 GB	very small, high quality loss
chat_gpt2_dpo-Q3_K_L.gguf	Q3_K_L	0.102 GB	small, substantial quality loss
chat_gpt2_dpo-Q4_0.gguf	Q4_0	0.107 GB	legacy; small, very high quality loss - prefer using Q3_K_M
chat_gpt2_dpo-Q4_K_S.gguf	Q4_K_S	0.107 GB	small, greater quality loss
chat_gpt2_dpo-Q4_K_M.gguf	Q4_K_M	0.113 GB	medium, balanced quality - recommended
chat_gpt2_dpo-Q5_0.gguf	Q5_0	0.122 GB	legacy; medium, balanced quality - prefer using Q4_K_M
chat_gpt2_dpo-Q5_K_S.gguf	Q5_K_S	0.122 GB	large, low quality loss - recommended
chat_gpt2_dpo-Q5_K_M.gguf	Q5_K_M	0.127 GB	large, very low quality loss - recommended
chat_gpt2_dpo-Q6_K.gguf	Q6_K	0.138 GB	very large, extremely low quality loss
chat_gpt2_dpo-Q8_0.gguf	Q8_0	0.178 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/chat_gpt2_dpo-GGUF --include "chat_gpt2_dpo-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/chat_gpt2_dpo-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'