README.md · tensorblock/Llama-3.2-SUN-2.5B-chat-GGUF at main

metadata

language:
  - en
license: llama3.2
library_name: transformers
base_model: meditsolutions/Llama-3.2-SUN-2.5B-chat
datasets:
  - argilla/OpenHermesPreferences
  - argilla/magpie-ultra-v0.1
  - argilla/Capybara-Preferences-Filtered
  - mlabonne/open-perfectblend
  - HuggingFaceTB/everyday-conversations-llama3.1-2k
  - WizardLMTeam/WizardLM_evol_instruct_V2_196k
  - ProlificAI/social-reasoning-rlhf
pipeline_tag: text-generation
tags:
  - TensorBlock
  - GGUF
model-index:
  - name: Llama-3.2-SUN-2.5B-chat
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 56.04
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.2-SUN-2.5B-chat
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 9.41
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.2-SUN-2.5B-chat
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 5.06
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.2-SUN-2.5B-chat
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 1.23
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.2-SUN-2.5B-chat
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 1.11
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.2-SUN-2.5B-chat
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 9.04
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=meditsolutions/Llama-3.2-SUN-2.5B-chat
          name: Open LLM Leaderboard

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meditsolutions/Llama-3.2-SUN-2.5B-chat - GGUF

This repo contains GGUF format model files for meditsolutions/Llama-3.2-SUN-2.5B-chat.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b4011.

Run them on the TensorBlock client using your local machine ↗

Prompt template

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

Cutting Knowledge Date: December 2023
Today Date: 25 Nov 2024

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Model file specification

Filename	Quant type	File Size	Description
Llama-3.2-SUN-2.5B-chat-Q2_K.gguf	Q2_K	1.025 GB	smallest, significant quality loss - not recommended for most purposes
Llama-3.2-SUN-2.5B-chat-Q3_K_S.gguf	Q3_K_S	1.173 GB	very small, high quality loss
Llama-3.2-SUN-2.5B-chat-Q3_K_M.gguf	Q3_K_M	1.271 GB	very small, high quality loss
Llama-3.2-SUN-2.5B-chat-Q3_K_L.gguf	Q3_K_L	1.355 GB	small, substantial quality loss
Llama-3.2-SUN-2.5B-chat-Q4_0.gguf	Q4_0	1.466 GB	legacy; small, very high quality loss - prefer using Q3_K_M
Llama-3.2-SUN-2.5B-chat-Q4_K_S.gguf	Q4_K_S	1.475 GB	small, greater quality loss
Llama-3.2-SUN-2.5B-chat-Q4_K_M.gguf	Q4_K_M	1.540 GB	medium, balanced quality - recommended
Llama-3.2-SUN-2.5B-chat-Q5_0.gguf	Q5_0	1.742 GB	legacy; medium, balanced quality - prefer using Q4_K_M
Llama-3.2-SUN-2.5B-chat-Q5_K_S.gguf	Q5_K_S	1.742 GB	large, low quality loss - recommended
Llama-3.2-SUN-2.5B-chat-Q5_K_M.gguf	Q5_K_M	1.780 GB	large, very low quality loss - recommended
Llama-3.2-SUN-2.5B-chat-Q6_K.gguf	Q6_K	2.036 GB	very large, extremely low quality loss
Llama-3.2-SUN-2.5B-chat-Q8_0.gguf	Q8_0	2.634 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/Llama-3.2-SUN-2.5B-chat-GGUF --include "Llama-3.2-SUN-2.5B-chat-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/Llama-3.2-SUN-2.5B-chat-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'