Transformers
PyTorch
Safetensors
GGUF
Bengali
mistral
text-generation-inference
unsloth
trl
sft
8-bit precision
Instructions to use barunsaha/misti with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use barunsaha/misti with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("barunsaha/misti", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use barunsaha/misti 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 barunsaha/misti:Q4_K_M # Run inference directly in the terminal: llama cli -hf barunsaha/misti:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf barunsaha/misti:Q4_K_M # Run inference directly in the terminal: llama cli -hf barunsaha/misti:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf barunsaha/misti:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf barunsaha/misti:Q4_K_M
Use Docker
docker model run hf.co/barunsaha/misti:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use barunsaha/misti with Ollama:
ollama run hf.co/barunsaha/misti:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use barunsaha/misti with Docker Model Runner:
docker model run hf.co/barunsaha/misti:Q4_K_M
- Lemonade
How to use barunsaha/misti with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull barunsaha/misti:Q4_K_M
Run and chat with the model
lemonade run user.misti-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download adapter_model.bin from barunsaha/misti: direct link, hf CLI and curl.
- Browser
- Download file 168 MB
-
https://huggingface.co/barunsaha/misti/resolve/main/adapter_model.bin
- Command line
-
hf download hf://barunsaha/misti/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/barunsaha/misti/resolve/main/adapter_model.bin
168 MB
- Xet hash:
- 24122d59f9358517d149021f7a77b1b3a174b42ae18e11df780f9cd9523fd629
- Size of remote file:
- 168 MB
- SHA256:
- 3a17b6a5ee35e36da85323b4fb946cb53c96667e039c6f3259b181019316fb9f
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