mradermacher
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README.md
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<!-- ### quantize_version: 2 -->
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<!-- ### output_tensor_quantised: 1 -->
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<!-- ### convert_type: hf -->
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<!-- ### vocab_type: -->
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<!-- ### tags: nicoboss -->
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weighted/imatrix quants of https://huggingface.co/Replete-AI/Replete-Coder-Llama3-8B
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---
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base_model: Replete-AI/Replete-Coder-Llama3-8B
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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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language:
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- en
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library_name: transformers
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license: other
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license_link: https://llama.meta.com/llama3/license/
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license_name: llama-3
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quantized_by: mradermacher
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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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---
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## About
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<!-- ### quantize_version: 2 -->
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<!-- ### output_tensor_quantised: 1 -->
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<!-- ### convert_type: hf -->
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<!-- ### vocab_type: -->
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<!-- ### tags: nicoboss -->
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weighted/imatrix quants of https://huggingface.co/Replete-AI/Replete-Coder-Llama3-8B
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<!-- provided-files -->
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static quants are available at https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-GGUF
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## Usage
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If you are unsure how to use GGUF files, refer to one of [TheBloke's
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READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
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more details, including on how to concatenate multi-part files.
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## Provided Quants
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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| Link | Type | Size/GB | Notes |
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|:-----|:-----|--------:|:------|
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
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| [GGUF](https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF/resolve/main/Replete-Coder-Llama3-8B.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
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And here are Artefact2's thoughts on the matter:
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https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
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## FAQ / Model Request
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See https://huggingface.co/mradermacher/model_requests for some answers to
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questions you might have and/or if you want some other model quantized.
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## Thanks
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I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
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me use its servers and providing upgrades to my workstation to enable
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this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
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<!-- end -->
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