Transformers
Safetensors
GGUF
English
mistral
text-generation-inference
unsloth
trl
sft
8-bit precision
Instructions to use Rohithqwerty/model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rohithqwerty/model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Rohithqwerty/model", dtype="auto") - llama-cpp-python
How to use Rohithqwerty/model with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Rohithqwerty/model", filename="model-unsloth.F16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Rohithqwerty/model with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Rohithqwerty/model:F16 # Run inference directly in the terminal: llama-cli -hf Rohithqwerty/model:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Rohithqwerty/model:F16 # Run inference directly in the terminal: llama-cli -hf Rohithqwerty/model: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 Rohithqwerty/model:F16 # Run inference directly in the terminal: ./llama-cli -hf Rohithqwerty/model: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 Rohithqwerty/model:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Rohithqwerty/model:F16
Use Docker
docker model run hf.co/Rohithqwerty/model:F16
- LM Studio
- Jan
- Ollama
How to use Rohithqwerty/model with Ollama:
ollama run hf.co/Rohithqwerty/model:F16
- Unsloth Studio new
How to use Rohithqwerty/model 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 Rohithqwerty/model 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 Rohithqwerty/model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Rohithqwerty/model to start chatting
- Docker Model Runner
How to use Rohithqwerty/model with Docker Model Runner:
docker model run hf.co/Rohithqwerty/model:F16
- Lemonade
How to use Rohithqwerty/model with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Rohithqwerty/model:F16
Run and chat with the model
lemonade run user.model-F16
List all available models
lemonade list
Trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
- special_tokens_map.json +30 -0
- tokenizer.model +3 -0
- tokenizer_config.json +0 -0
special_tokens_map.json
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{
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"bos_token": {
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"content": "<s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "</s>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "[control_768]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:37f00374dea48658ee8f5d0f21895b9bc55cb0103939607c8185bfd1c6ca1f89
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size 587404
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tokenizer_config.json
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