Instructions to use mradermacher/Replete-Coder-Llama3-8B-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/Replete-Coder-Llama3-8B-i1-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/Replete-Coder-Llama3-8B-i1-GGUF", dtype="auto") - llama-cpp-python
How to use mradermacher/Replete-Coder-Llama3-8B-i1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mradermacher/Replete-Coder-Llama3-8B-i1-GGUF", filename="Replete-Coder-Llama3-8B.i1-IQ1_M.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/Replete-Coder-Llama3-8B-i1-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mradermacher/Replete-Coder-Llama3-8B-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mradermacher/Replete-Coder-Llama3-8B-i1-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mradermacher/Replete-Coder-Llama3-8B-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mradermacher/Replete-Coder-Llama3-8B-i1-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf mradermacher/Replete-Coder-Llama3-8B-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/Replete-Coder-Llama3-8B-i1-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf mradermacher/Replete-Coder-Llama3-8B-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/Replete-Coder-Llama3-8B-i1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/Replete-Coder-Llama3-8B-i1-GGUF with Ollama:
ollama run hf.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF:Q4_K_M
- Unsloth Studio
How to use mradermacher/Replete-Coder-Llama3-8B-i1-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mradermacher/Replete-Coder-Llama3-8B-i1-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for mradermacher/Replete-Coder-Llama3-8B-i1-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mradermacher/Replete-Coder-Llama3-8B-i1-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use mradermacher/Replete-Coder-Llama3-8B-i1-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/Replete-Coder-Llama3-8B-i1-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/Replete-Coder-Llama3-8B-i1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/Replete-Coder-Llama3-8B-i1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Replete-Coder-Llama3-8B-i1-GGUF-Q4_K_M
List all available models
lemonade list
File size: 6,347 Bytes
b04e254 0811669 b04e254 075f2de b04e254 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | ---
base_model: Replete-AI/Replete-Coder-Llama3-8B
datasets:
- Replete-AI/code_bagel_hermes-2.5
- Replete-AI/code_bagel
- Replete-AI/OpenHermes-2.5-Uncensored
- teknium/OpenHermes-2.5
- layoric/tiny-codes-alpaca
- glaiveai/glaive-code-assistant-v3
- ajibawa-2023/Code-290k-ShareGPT
- TIGER-Lab/MathInstruct
- chargoddard/commitpack-ft-instruct-rated
- iamturun/code_instructions_120k_alpaca
- ise-uiuc/Magicoder-Evol-Instruct-110K
- cognitivecomputations/dolphin-coder
- nickrosh/Evol-Instruct-Code-80k-v1
- coseal/CodeUltraFeedback_binarized
- glaiveai/glaive-function-calling-v2
- CyberNative/Code_Vulnerability_Security_DPO
- jondurbin/airoboros-2.2
- camel-ai
- lmsys/lmsys-chat-1m
- CollectiveCognition/chats-data-2023-09-22
- CoT-Alpaca-GPT4
- WizardLM/WizardLM_evol_instruct_70k
- WizardLM/WizardLM_evol_instruct_V2_196k
- teknium/GPT4-LLM-Cleaned
- GPTeacher
- OpenGPT
- meta-math/MetaMathQA
- Open-Orca/SlimOrca
- garage-bAInd/Open-Platypus
- anon8231489123/ShareGPT_Vicuna_unfiltered
- Unnatural-Instructions-GPT4
language:
- en
library_name: transformers
license: other
license_link: https://llama.meta.com/llama3/license/
license_name: llama-3
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- llama
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Replete-AI/Replete-Coder-Llama3-8B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Replete-Coder-Llama3-8B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [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 |
| [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 |
| [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 | |
| [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 | |
| [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 | |
| [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 | |
| [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 |
| [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 |
| [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 | |
| [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 |
| [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* |
| [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 | |
| [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 |
| [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 |
| [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 | |
| [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 |
| [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 |
| [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 |
| [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 | |
| [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 | |
| [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 |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
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