Instructions to use Elkheta/elkheta-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Elkheta/elkheta-model 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 Elkheta/elkheta-model:Q4_K_M # Run inference directly in the terminal: llama cli -hf Elkheta/elkheta-model:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Elkheta/elkheta-model:Q4_K_M # Run inference directly in the terminal: llama cli -hf Elkheta/elkheta-model: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 Elkheta/elkheta-model:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Elkheta/elkheta-model: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 Elkheta/elkheta-model:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Elkheta/elkheta-model:Q4_K_M
Use Docker
docker model run hf.co/Elkheta/elkheta-model:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use Elkheta/elkheta-model with Ollama:
ollama run hf.co/Elkheta/elkheta-model:Q4_K_M
- Unsloth Studio
How to use Elkheta/elkheta-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 Elkheta/elkheta-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 Elkheta/elkheta-model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Elkheta/elkheta-model to start chatting
- Docker Model Runner
How to use Elkheta/elkheta-model with Docker Model Runner:
docker model run hf.co/Elkheta/elkheta-model:Q4_K_M
- Lemonade
How to use Elkheta/elkheta-model with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Elkheta/elkheta-model:Q4_K_M
Run and chat with the model
lemonade run user.elkheta-model-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Upload model trained with Unsloth
Browse filesUpload model trained with Unsloth 2x faster
- .gitattributes +1 -0
- chat_template.jinja +6 -0
- tokenizer.json +3 -0
- tokenizer_config.json +45 -0
.gitattributes
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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chat_template.jinja
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{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
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You are a helpful assistant.<|im_end|>
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' }}{% endif %}{{'<|im_start|>' + message['role'] + '
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' + message['content'] + '<|im_end|>' + '
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'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
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' }}{% endif %}
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:2bdac196ac6511f06ae7ed6153d3690bd4a8084aad51c2f152d6f42682839c75
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size 11418536
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tokenizer_config.json
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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"bos_token": null,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"errors": "replace",
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"extra_special_tokens": [
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"<|im_start|>",
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"is_local": false,
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"model_max_length": 32768,
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"pad_token": "<|endoftext|>",
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"padding_side": "left",
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"split_special_tokens": false,
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"tokenizer_class": "Qwen2Tokenizer",
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"unk_token": null,
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"added_tokens_decoder": {
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"content": "<|endoftext|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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},
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"151644": {
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"content": "<|im_start|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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},
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"151645": {
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"content": "<|im_end|>",
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"single_word": false,
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"lstrip": false,
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"rstrip": false,
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"normalized": false,
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"special": true
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