Instructions to use bartowski/TableLLM-13b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use bartowski/TableLLM-13b-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bartowski/TableLLM-13b-GGUF", filename="TableLLM-13b-IQ3_M.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 bartowski/TableLLM-13b-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf bartowski/TableLLM-13b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/TableLLM-13b-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 bartowski/TableLLM-13b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/TableLLM-13b-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 bartowski/TableLLM-13b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/TableLLM-13b-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 bartowski/TableLLM-13b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/TableLLM-13b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/TableLLM-13b-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/TableLLM-13b-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/TableLLM-13b-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/TableLLM-13b-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bartowski/TableLLM-13b-GGUF:Q4_K_M
- Ollama
How to use bartowski/TableLLM-13b-GGUF with Ollama:
ollama run hf.co/bartowski/TableLLM-13b-GGUF:Q4_K_M
- Unsloth Studio new
How to use bartowski/TableLLM-13b-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 bartowski/TableLLM-13b-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 bartowski/TableLLM-13b-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bartowski/TableLLM-13b-GGUF to start chatting
- Docker Model Runner
How to use bartowski/TableLLM-13b-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/TableLLM-13b-GGUF:Q4_K_M
- Lemonade
How to use bartowski/TableLLM-13b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/TableLLM-13b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.TableLLM-13b-GGUF-Q4_K_M
List all available models
lemonade list
Llamacpp quants
Browse files- .gitattributes +16 -0
- README.md +42 -0
- TableLLM-13b-IQ3_M.gguf +3 -0
- TableLLM-13b-IQ3_S.gguf +3 -0
- TableLLM-13b-IQ4_NL.gguf +3 -0
- TableLLM-13b-IQ4_XS.gguf +3 -0
- TableLLM-13b-Q2_K.gguf +3 -0
- TableLLM-13b-Q3_K_L.gguf +3 -0
- TableLLM-13b-Q3_K_M.gguf +3 -0
- TableLLM-13b-Q3_K_S.gguf +3 -0
- TableLLM-13b-Q4_0.gguf +3 -0
- TableLLM-13b-Q4_K_M.gguf +3 -0
- TableLLM-13b-Q4_K_S.gguf +3 -0
- TableLLM-13b-Q5_0.gguf +3 -0
- TableLLM-13b-Q5_K_M.gguf +3 -0
- TableLLM-13b-Q5_K_S.gguf +3 -0
- TableLLM-13b-Q6_K.gguf +3 -0
- TableLLM-13b-Q8_0.gguf +3 -0
.gitattributes
CHANGED
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@@ -33,3 +33,19 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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TableLLM-13b-IQ3_M.gguf filter=lfs diff=lfs merge=lfs -text
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TableLLM-13b-IQ3_S.gguf filter=lfs diff=lfs merge=lfs -text
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TableLLM-13b-IQ4_NL.gguf filter=lfs diff=lfs merge=lfs -text
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TableLLM-13b-IQ4_XS.gguf filter=lfs diff=lfs merge=lfs -text
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TableLLM-13b-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
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TableLLM-13b-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text
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TableLLM-13b-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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TableLLM-13b-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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TableLLM-13b-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text
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TableLLM-13b-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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TableLLM-13b-Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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TableLLM-13b-Q5_0.gguf filter=lfs diff=lfs merge=lfs -text
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TableLLM-13b-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text
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TableLLM-13b-Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text
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TableLLM-13b-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text
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TableLLM-13b-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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license: llama2
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datasets:
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- RUCKBReasoning/TableLLM-SFT
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| 5 |
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language:
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- en
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tags:
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- Table
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- QA
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- Code
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| 11 |
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quantized_by: bartowski
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pipeline_tag: text-generation
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---
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| 15 |
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## Llamacpp Quantizations of TableLLM-13b
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Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2589">b2589</a> for quantization.
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Original model: https://huggingface.co/RUCKBReasoning/TableLLM-13b
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Download a file (not the whole branch) from below:
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| 23 |
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| Filename | Quant type | File Size | Description |
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| 24 |
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| -------- | ---------- | --------- | ----------- |
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| 25 |
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| [TableLLM-13b-Q8_0.gguf](https://huggingface.co/bartowski/TableLLM-13b-GGUF/blob/main/TableLLM-13b-Q8_0.gguf) | Q8_0 | 13.83GB | Extremely high quality, generally unneeded but max available quant. |
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| 26 |
+
| [TableLLM-13b-Q6_K.gguf](https://huggingface.co/bartowski/TableLLM-13b-GGUF/blob/main/TableLLM-13b-Q6_K.gguf) | Q6_K | 10.67GB | Very high quality, near perfect, *recommended*. |
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| 27 |
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| [TableLLM-13b-Q5_K_M.gguf](https://huggingface.co/bartowski/TableLLM-13b-GGUF/blob/main/TableLLM-13b-Q5_K_M.gguf) | Q5_K_M | 9.23GB | High quality, very usable. |
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| 28 |
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| [TableLLM-13b-Q5_K_S.gguf](https://huggingface.co/bartowski/TableLLM-13b-GGUF/blob/main/TableLLM-13b-Q5_K_S.gguf) | Q5_K_S | 8.97GB | High quality, very usable. |
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| 29 |
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| [TableLLM-13b-Q5_0.gguf](https://huggingface.co/bartowski/TableLLM-13b-GGUF/blob/main/TableLLM-13b-Q5_0.gguf) | Q5_0 | 8.97GB | High quality, older format, generally not recommended. |
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| 30 |
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| [TableLLM-13b-Q4_K_M.gguf](https://huggingface.co/bartowski/TableLLM-13b-GGUF/blob/main/TableLLM-13b-Q4_K_M.gguf) | Q4_K_M | 7.86GB | Good quality, uses about 4.83 bits per weight. |
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| [TableLLM-13b-Q4_K_S.gguf](https://huggingface.co/bartowski/TableLLM-13b-GGUF/blob/main/TableLLM-13b-Q4_K_S.gguf) | Q4_K_S | 7.42GB | Slightly lower quality with small space savings. |
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| [TableLLM-13b-IQ4_NL.gguf](https://huggingface.co/bartowski/TableLLM-13b-GGUF/blob/main/TableLLM-13b-IQ4_NL.gguf) | IQ4_NL | 7.41GB | Decent quality, similar to Q4_K_S, new method of quanting, |
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| [TableLLM-13b-IQ4_XS.gguf](https://huggingface.co/bartowski/TableLLM-13b-GGUF/blob/main/TableLLM-13b-IQ4_XS.gguf) | IQ4_XS | 7.01GB | Decent quality, new method with similar performance to Q4. |
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| [TableLLM-13b-Q4_0.gguf](https://huggingface.co/bartowski/TableLLM-13b-GGUF/blob/main/TableLLM-13b-Q4_0.gguf) | Q4_0 | 7.36GB | Decent quality, older format, generally not recommended. |
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| [TableLLM-13b-Q3_K_L.gguf](https://huggingface.co/bartowski/TableLLM-13b-GGUF/blob/main/TableLLM-13b-Q3_K_L.gguf) | Q3_K_L | 6.92GB | Lower quality but usable, good for low RAM availability. |
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| [TableLLM-13b-Q3_K_M.gguf](https://huggingface.co/bartowski/TableLLM-13b-GGUF/blob/main/TableLLM-13b-Q3_K_M.gguf) | Q3_K_M | 6.33GB | Even lower quality. |
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| [TableLLM-13b-IQ3_M.gguf](https://huggingface.co/bartowski/TableLLM-13b-GGUF/blob/main/TableLLM-13b-IQ3_M.gguf) | IQ3_M | 5.98GB | Medium-low quality, new method with decent performance. |
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| [TableLLM-13b-IQ3_S.gguf](https://huggingface.co/bartowski/TableLLM-13b-GGUF/blob/main/TableLLM-13b-IQ3_S.gguf) | IQ3_S | 5.65GB | Lower quality, new method with decent performance, recommended over Q3 quants. |
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| 39 |
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| [TableLLM-13b-Q3_K_S.gguf](https://huggingface.co/bartowski/TableLLM-13b-GGUF/blob/main/TableLLM-13b-Q3_K_S.gguf) | Q3_K_S | 5.65GB | Low quality, not recommended. |
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| [TableLLM-13b-Q2_K.gguf](https://huggingface.co/bartowski/TableLLM-13b-GGUF/blob/main/TableLLM-13b-Q2_K.gguf) | Q2_K | 4.85GB | Extremely low quality, *not* recommended.
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| 42 |
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Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
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TableLLM-13b-IQ3_M.gguf
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TableLLM-13b-IQ3_S.gguf
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TableLLM-13b-Q2_K.gguf
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TableLLM-13b-Q3_K_L.gguf
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TableLLM-13b-Q3_K_M.gguf
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TableLLM-13b-Q4_0.gguf
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