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language:
  - en
license: apache-2.0
library_name: transformers
tags:
  - transformers
  - TensorBlock
  - GGUF
datasets:
  - mwitiderrick/AlpacaCode
base_model: mwitiderrick/open_llama_3b_code_instruct_0.1
inference: true
model_type: llama
prompt_template: |
  ### Instruction:\n
  {prompt}
  ### Response:
created_by: mwitiderrick
pipeline_tag: text-generation
model-index:
  - name: mwitiderrick/open_llama_3b_instruct_v_0.2
    results:
      - task:
          type: text-generation
        dataset:
          name: hellaswag
          type: hellaswag
        metrics:
          - type: hellaswag (0-Shot)
            value: 0.6581
            name: hellaswag(0-Shot)
      - task:
          type: text-generation
        dataset:
          name: winogrande
          type: winogrande
        metrics:
          - type: winogrande (0-Shot)
            value: 0.6267
            name: winogrande(0-Shot)
      - task:
          type: text-generation
        dataset:
          name: arc_challenge
          type: arc_challenge
        metrics:
          - type: arc_challenge (0-Shot)
            value: 0.3712
            name: arc_challenge(0-Shot)
        source:
          url: https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2
          name: open_llama_3b_instruct_v_0.2 model card
      - 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: 41.21
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
          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: 66.96
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
          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: 27.82
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
          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: 35.01
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
          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: 65.43
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
          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: 1.9
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1
          name: Open LLM Leaderboard

mwitiderrick/open_llama_3b_code_instruct_0.1 - GGUF

This repo contains GGUF format model files for mwitiderrick/open_llama_3b_code_instruct_0.1.

The files were quantized using machines provided by TensorBlock, and they are compatible with llama.cpp as of commit b5165.

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Prompt template

Unable to determine prompt format automatically. Please check the original model repository for the correct prompt format.

Model file specification

Filename	Quant type	File Size	Description
open_llama_3b_code_instruct_0.1-Q2_K.gguf	Q2_K	1.980 GB	smallest, significant quality loss - not recommended for most purposes
open_llama_3b_code_instruct_0.1-Q3_K_S.gguf	Q3_K_S	1.980 GB	very small, high quality loss
open_llama_3b_code_instruct_0.1-Q3_K_M.gguf	Q3_K_M	2.139 GB	very small, high quality loss
open_llama_3b_code_instruct_0.1-Q3_K_L.gguf	Q3_K_L	2.215 GB	small, substantial quality loss
open_llama_3b_code_instruct_0.1-Q4_0.gguf	Q4_0	1.980 GB	legacy; small, very high quality loss - prefer using Q3_K_M
open_llama_3b_code_instruct_0.1-Q4_K_S.gguf	Q4_K_S	2.403 GB	small, greater quality loss
open_llama_3b_code_instruct_0.1-Q4_K_M.gguf	Q4_K_M	2.580 GB	medium, balanced quality - recommended
open_llama_3b_code_instruct_0.1-Q5_0.gguf	Q5_0	2.395 GB	legacy; medium, balanced quality - prefer using Q4_K_M
open_llama_3b_code_instruct_0.1-Q5_K_S.gguf	Q5_K_S	2.603 GB	large, low quality loss - recommended
open_llama_3b_code_instruct_0.1-Q5_K_M.gguf	Q5_K_M	2.757 GB	large, very low quality loss - recommended
open_llama_3b_code_instruct_0.1-Q6_K.gguf	Q6_K	3.642 GB	very large, extremely low quality loss
open_llama_3b_code_instruct_0.1-Q8_0.gguf	Q8_0	3.642 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/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF --include "open_llama_3b_code_instruct_0.1-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/mwitiderrick_open_llama_3b_code_instruct_0.1-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'