Instructions to use qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF", dtype="auto")

llama-cpp-python

How to use qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF",
	filename="replete-coder-qwen2-1.5b-bf16.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-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 qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-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 qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-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 qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF with Ollama:
```
ollama run hf.co/qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF:Q4_K_M
```

Unsloth Studio

How to use qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-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 qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-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 qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF with Docker Model Runner:
```
docker model run hf.co/qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF:Q4_K_M
```

Lemonade

How to use qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull qwp4w3hyb/Replete-Coder-Qwen2-1.5b-iMat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Replete-Coder-Qwen2-1.5b-iMat-GGUF-Q4_K_M

List all available models

lemonade list

Replete-Coder-Qwen2-1.5b-iMat-GGUF / README.md

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	---
	license: apache-2.0
	base_model: Replete-AI/Replete-Coder-Qwen2-1.5b
	tags:
	- text-generation-inference
	- transformers
	- unsloth
	- qwen2
	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
	---

	# Quant Infos

	- quants done with an importance matrix for improved quantization loss
	- ggufs & imatrix generated from bf16 for "optimal" accuracy loss
	- Wide coverage of different gguf quant types from Q\_8\_0 down to IQ1\_S
	- Quantized with [llama.cpp](https://github.com/ggerganov/llama.cpp) commit [4bfe50f741479c1df1c377260c3ff5702586719e](https://github.com/ggerganov/llama.cpp/commit/4bfe50f741479c1df1c377260c3ff5702586719e) (master as of 2024-06-11)
	- Imatrix generated with [this](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8) multi-purpose dataset by [bartowski](https://huggingface.co/bartowski).
	```
	./imatrix -c 512 -m $model_name-bf16.gguf -f calibration_datav3.txt -o $model_name.imatrix
	```

	# Original Model Card:

	# Replete-Coder-Qwen2-1.5b
	Finetuned by: Rombodawg
	### More than just a coding model!
	Although Replete-Coder has amazing coding capabilities, its trained on vaste amount of non-coding data, fully cleaned and uncensored. Dont just use it for coding, use it for all your needs! We are truly trying to make the GPT killer!
	![image/png](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/-0dERC793D9XeFsJ9uHbx.png)

	Thank you to TensorDock for sponsoring Replete-Coder-llama3-8b and Replete-Coder-Qwen2-1.5b
	you can check out their website for cloud compute rental bellow.
	- https://tensordock.com
	__________________________________________________________________________________________________
	Replete-Coder-Qwen2-1.5b is a general purpose model that is specially trained in coding in over 100 coding languages. The data used to train the model contains 25% non-code instruction data and 75% coding instruction data totaling up to 3.9 million lines, roughly 1 billion tokens, or 7.27gb of instruct data. The data used to train this model was 100% uncensored, then fully deduplicated, before training happened.

	The Replete-Coder models (including Replete-Coder-llama3-8b and Replete-Coder-Qwen2-1.5b) feature the following:

	- Advanced coding capabilities in over 100 coding languages
	- Advanced code translation (between languages)
	- Security and vulnerability prevention related coding capabilities
	- General purpose use
	- Uncensored use
	- Function calling
	- Advanced math use
	- Use on low end (8b) and mobile (1.5b) platforms

	Notice: Replete-Coder series of models are fine-tuned on a context window of 8192 tokens. Performance past this context window is not guaranteed.

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/ADHZysQCKxiSordZRwuj_.png)
	__________________________________________________________________________________________________

	You can find the 25% non-coding instruction below:

	- https://huggingface.co/datasets/Replete-AI/OpenHermes-2.5-Uncensored

	And the 75% coding specific instruction data below:

	- https://huggingface.co/datasets/Replete-AI/code_bagel

	These two datasets were combined to create the final dataset for training, which is linked below:

	- https://huggingface.co/datasets/Replete-AI/code_bagel_hermes-2.5
	__________________________________________________________________________________________________
	## Prompt Template: ChatML
	```
	<\|im_start\|>system
	{}<\|im_end\|>

	<\|im_start\|>user
	{}<\|im_end\|>

	<\|im_start\|>assistant
	{}
	```
	Note: The system prompt varies in training data, but the most commonly used one is:
	```
	Below is an instruction that describes a task, Write a response that appropriately completes the request.
	```
	End token:
	```
	<\|endoftext\|>
	```
	__________________________________________________________________________________________________
	Thank you to the community for your contributions to the Replete-AI/code_bagel_hermes-2.5 dataset. Without the participation of so many members making their datasets free and open source for any to use, this amazing AI model wouldn't be possible.

	Extra special thanks to Teknium for the Open-Hermes-2.5 dataset and jondurbin for the bagel dataset and the naming idea for the code_bagel series of datasets. You can find both of their huggingface accounts linked below:

	- https://huggingface.co/teknium
	- https://huggingface.co/jondurbin

	Another special thanks to unsloth for being the main method of training for Replete-Coder. Bellow you can find their github, as well as the special Replete-Ai secret sause (Unsloth + Qlora + Galore) colab code document that was used to train this model.

	- https://github.com/unslothai/unsloth
	- https://colab.research.google.com/drive/1eXGqy5M--0yW4u0uRnmNgBka-tDk2Li0?usp=sharing
	__________________________________________________________________________________________________

	## Join the Replete-Ai discord! We are a great and Loving community!

	- https://discord.gg/ZZbnsmVnjD