Instructions to use N-Bot-Int/MistThena7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use N-Bot-Int/MistThena7B-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("N-Bot-Int/MistThena7B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use N-Bot-Int/MistThena7B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf N-Bot-Int/MistThena7B-GGUF:F16 # Run inference directly in the terminal: llama cli -hf N-Bot-Int/MistThena7B-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf N-Bot-Int/MistThena7B-GGUF:F16 # Run inference directly in the terminal: llama cli -hf N-Bot-Int/MistThena7B-GGUF:F16
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 N-Bot-Int/MistThena7B-GGUF:F16 # Run inference directly in the terminal: ./llama-cli -hf N-Bot-Int/MistThena7B-GGUF:F16
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 N-Bot-Int/MistThena7B-GGUF:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf N-Bot-Int/MistThena7B-GGUF:F16
Use Docker
docker model run hf.co/N-Bot-Int/MistThena7B-GGUF:F16
- LM Studio
- Jan
- Ollama
How to use N-Bot-Int/MistThena7B-GGUF with Ollama:
ollama run hf.co/N-Bot-Int/MistThena7B-GGUF:F16
- Unsloth Desktop
- Docker Model Runner
How to use N-Bot-Int/MistThena7B-GGUF with Docker Model Runner:
docker model run hf.co/N-Bot-Int/MistThena7B-GGUF:F16
- Lemonade
How to use N-Bot-Int/MistThena7B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull N-Bot-Int/MistThena7B-GGUF:F16
Run and chat with the model
lemonade run user.MistThena7B-GGUF-F16
List all available models
lemonade list
- Atomic Chat
Update README.md
Browse files
README.md
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@@ -3,20 +3,38 @@ base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
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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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- mistral
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- gguf
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license: apache-2.0
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language:
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- en
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---
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# Uploaded model
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- **Developed by:** N-Bot-Int
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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- mistral
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- rp
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- gguf
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language:
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- en
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license: apache-2.0
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datasets:
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- N-Bot-Int/Iris-Uncensored-R1
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- N-Bot-Int/Moshpit-Combined-R2-Uncensored
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- N-Bot-Int/Mushed-Dataset-Uncensored
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- N-Bot-Int/Muncher-R1-Uncensored
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- unalignment/toxic-dpo-v0.1
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library_name: transformers
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---
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<a href="https://imgbb.com/"><img src="https://raw.githubusercontent.com/Nexus-Network-Interactives/HuggingfacePage/refs/heads/main/MistThena7BGGUF.webp" alt="1741962015516" border="0"></a>
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# GGUF Version
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**GGUF** with Quants! Allowing you to run models using KoboldCPP and other AI Environments!
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# Quantizations:
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| Quant Type | Benefits | Cons |
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|---------------|---------------------------------------------------|---------------------------------------------------|
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| **Q4_K_M** | β
Smallest size (fastest inference) | β Lowest accuracy compared to other quants |
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Requires the least VRAM/RAM | β May struggle with complex reasoning |
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Ideal for edge devices & low-resource setups | β Can produce slightly degraded text quality |
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| **Q5_K_M** | β
Better accuracy than Q4, while still compact | β Slightly larger model size than Q4 |
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Good balance between speed and precision | β Needs a bit more VRAM than Q4 |
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Works well on mid-range GPUs | β Still not as accurate as higher-bit models |
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| **Q8_0** | β
Highest accuracy (closest to full model) | β Requires significantly more VRAM/RAM |
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Best for complex reasoning & detailed outputs | β Slower inference compared to Q4 & Q5 |
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Suitable for high-end GPUs & serious workloads | β Larger file size (takes more storage) |
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# Model Details:
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Read the Model details on huggingface
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<link src="https://huggingface.co/N-Bot-Int/OpenElla3-Llama3.2B">Model Detail Here!</link>
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