measurement.json
Browse files- README.md +222 -0
- measurement.json +0 -0
README.md
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---
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license: other
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license_name: llama-3
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license_link: https://llama.meta.com/llama3/license/
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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datasets:
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- Replete-AI/code_bagel_hermes-2.5
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- Replete-AI/code_bagel
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- Replete-AI/OpenHermes-2.5-Uncensored
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- teknium/OpenHermes-2.5
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- layoric/tiny-codes-alpaca
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- glaiveai/glaive-code-assistant-v3
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- ajibawa-2023/Code-290k-ShareGPT
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- TIGER-Lab/MathInstruct
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- chargoddard/commitpack-ft-instruct-rated
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- iamturun/code_instructions_120k_alpaca
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- ise-uiuc/Magicoder-Evol-Instruct-110K
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- cognitivecomputations/dolphin-coder
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- nickrosh/Evol-Instruct-Code-80k-v1
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- coseal/CodeUltraFeedback_binarized
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- glaiveai/glaive-function-calling-v2
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- CyberNative/Code_Vulnerability_Security_DPO
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- jondurbin/airoboros-2.2
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- camel-ai
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- lmsys/lmsys-chat-1m
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- CollectiveCognition/chats-data-2023-09-22
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- CoT-Alpaca-GPT4
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- WizardLM/WizardLM_evol_instruct_70k
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- WizardLM/WizardLM_evol_instruct_V2_196k
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- teknium/GPT4-LLM-Cleaned
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- GPTeacher
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- OpenGPT
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- meta-math/MetaMathQA
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- Open-Orca/SlimOrca
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- garage-bAInd/Open-Platypus
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- anon8231489123/ShareGPT_Vicuna_unfiltered
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- Unnatural-Instructions-GPT4
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model-index:
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- name: Replete-Coder-llama3-8b
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results:
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- task:
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name: HumanEval
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type: text-generation
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dataset:
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type: openai_humaneval
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name: HumanEval
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metrics:
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- name: pass@1
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type: pass@1
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value:
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verified: false
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- task:
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name: AI2 Reasoning Challenge
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type: text-generation
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dataset:
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name: AI2 Reasoning Challenge (25-Shot)
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type: ai2_arc
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config: ARC-Challenge
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split: test
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args:
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num_few_shot: 25
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metrics:
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- type: accuracy
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value:
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name: normalized accuracy
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source:
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url: https://www.placeholderurl.com
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name: Open LLM Leaderboard
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- task:
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name: Text Generation
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type: text-generation
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dataset:
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name: HellaSwag (10-Shot)
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type: hellaswag
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split: validation
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args:
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num_few_shot: 10
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metrics:
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- type: accuracy
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value:
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name: normalized accuracy
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source:
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url: https://www.placeholderurl.com
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name: Open LLM Leaderboard
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- task:
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name: Text Generation
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type: text-generation
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dataset:
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name: MMLU (5-Shot)
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type: cais/mmlu
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config: all
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: accuracy
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value:
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name: accuracy
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source:
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url: https://www.placeholderurl.com
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name: Open LLM Leaderboard
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- task:
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name: Text Generation
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type: text-generation
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dataset:
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name: TruthfulQA (0-shot)
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type: truthful_qa
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config: multiple_choice
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split: validation
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args:
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num_few_shot: 0
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metrics:
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- type: multiple_choice_accuracy
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value:
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source:
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url: https://www.placeholderurl.com
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name: Open LLM Leaderboard
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- task:
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name: Text Generation
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type: text-generation
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dataset:
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name: Winogrande (5-shot)
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type: winogrande
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config: winogrande_xl
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split: validation
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args:
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num_few_shot: 5
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metrics:
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- type: accuracy
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value:
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name: accuracy
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source:
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url: https://www.placeholderurl.com
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name: Open LLM Leaderboard
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- task:
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name: Text Generation
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type: text-generation
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dataset:
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name: GSM8k (5-shot)
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type: gsm8k
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config: main
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split: test
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args:
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num_few_shot: 5
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metrics:
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- type: accuracy
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value:
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name: accuracy
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source:
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url: https://www.placeholderurl.com
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name: Open LLM Leaderboard
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quantized_by: bartowski
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pipeline_tag: text-generation
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---
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## Exllama v2 Quantizations of Replete-Coder-Llama3-8B
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Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.1.6">turboderp's ExLlamaV2 v0.1.6</a> for quantization.
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<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
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Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
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Original model: https://huggingface.co/Replete-AI/Replete-Coder-Llama3-8B
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## Prompt format
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No chat template specified so default is used. This may be incorrect, check original model card for details.
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```
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<|im_start|>system
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{system_prompt}<|im_end|>
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant
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```
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## Available sizes
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| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (8K) | VRAM (16k) | VRAM (32k) | Description |
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| ----- | ---- | ------- | ------ | ------ | ------ | ------ | ------------ |
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| [8_0](https://huggingface.co/bartowski/Replete-Coder-Llama3-8B-exl2/tree/8_0) | 8.0 | 8.0 | 10.1 GB | 10.5 GB | 11.5 GB | 13.6 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
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| [6_5](https://huggingface.co/bartowski/Replete-Coder-Llama3-8B-exl2/tree/6_5) | 6.5 | 8.0 | 8.9 GB | 9.3 GB | 10.3 GB | 12.4 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
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| [5_0](https://huggingface.co/bartowski/Replete-Coder-Llama3-8B-exl2/tree/5_0) | 5.0 | 6.0 | 7.7 GB | 8.1 GB | 9.1 GB | 11.2 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
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| [4_25](https://huggingface.co/bartowski/Replete-Coder-Llama3-8B-exl2/tree/4_25) | 4.25 | 6.0 | 7.0 GB | 7.4 GB | 8.4 GB | 10.5 GB | GPTQ equivalent bits per weight, slightly higher quality. |
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| [3_5](https://huggingface.co/bartowski/Replete-Coder-Llama3-8B-exl2/tree/3_5) | 3.5 | 6.0 | 6.4 GB | 6.8 GB | 7.8 GB | 9.9 GB | Lower quality, only use if you have to. |
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## Download instructions
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With git:
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```shell
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git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Replete-Coder-Llama3-8B-exl2 Replete-Coder-Llama3-8B-exl2-6_5
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```
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With huggingface hub (credit to TheBloke for instructions):
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```shell
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pip3 install huggingface-hub
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```
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To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch:
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Linux:
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```shell
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huggingface-cli download bartowski/Replete-Coder-Llama3-8B-exl2 --revision 6_5 --local-dir Replete-Coder-Llama3-8B-exl2-6_5
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```
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Windows (which apparently doesn't like _ in folders sometimes?):
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```shell
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huggingface-cli download bartowski/Replete-Coder-Llama3-8B-exl2 --revision 6_5 --local-dir Replete-Coder-Llama3-8B-exl2-6.5
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```
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Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
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measurement.json
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The diff for this file is too large to render.
See raw diff
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