Text Generation
GGUF
English
llama.cpp
bitnet
ternary
1.58-bit
quantized
q4_k_m
edge
efficient-inference
cpu
tool-calling
Instructions to use Qapdex/SLM750-Edge-1.58-bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Qapdex/SLM750-Edge-1.58-bit with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Qapdex/SLM750-Edge-1.58-bit", filename="quantized_q4km.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Qapdex/SLM750-Edge-1.58-bit 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 Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT # Run inference directly in the terminal: llama cli -hf Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT # Run inference directly in the terminal: llama cli -hf Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
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 Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT # Run inference directly in the terminal: ./llama-cli -hf Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
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 Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT # Run inference directly in the terminal: ./build/bin/llama-cli -hf Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
Use Docker
docker model run hf.co/Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
- LM Studio
- Jan
- vLLM
How to use Qapdex/SLM750-Edge-1.58-bit with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Qapdex/SLM750-Edge-1.58-bit" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Qapdex/SLM750-Edge-1.58-bit", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
- Ollama
How to use Qapdex/SLM750-Edge-1.58-bit with Ollama:
ollama run hf.co/Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
- Unsloth Studio
How to use Qapdex/SLM750-Edge-1.58-bit 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 Qapdex/SLM750-Edge-1.58-bit 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 Qapdex/SLM750-Edge-1.58-bit to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Qapdex/SLM750-Edge-1.58-bit to start chatting
- Atomic Chat new
- Docker Model Runner
How to use Qapdex/SLM750-Edge-1.58-bit with Docker Model Runner:
docker model run hf.co/Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
- Lemonade
How to use Qapdex/SLM750-Edge-1.58-bit with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Qapdex/SLM750-Edge-1.58-bit:Q4_K_M_QUANT
Run and chat with the model
lemonade run user.SLM750-Edge-1.58-bit-Q4_K_M_QUANT
List all available models
lemonade list
| // These tests may take a long time! | |
| // They are to prove that conversion from double to float of various functions in ggml.c doesn't affect the result. | |
| // This is done by checking all finite (non-NaN, non-infinite) floats. | |
| // ggml.c::quantize_row_q4_0_ref | |
| inline static uint8_t round_orig(float v0) { return ((int8_t) (round(v0))) + 8; } | |
| // ggml.c::ggml_silu_f32 | |
| inline static float silu_orig(float x) { | |
| return x/(1.0 + exp(-x)); | |
| } | |
| // ggml.c::quantize_row_q4_0_ref | |
| inline static uint8_t round_float(float v0) { return (int8_t)roundf(v0) + 8; } | |
| // ggml.c::ggml_silu_f32 | |
| inline static float silu_float(float x) { | |
| return x/(1.0f + expf(-x)); | |
| } | |
| int main(void) { | |
| uint32_t x = UINT32_MAX; | |
| do { | |
| float f; | |
| memcpy(&f, &x, sizeof(x)); | |
| assert(!std::isfinite(f) || (round_orig(f) == round_float(f))); | |
| } while (x--); | |
| // GELU and SILU implementations are used with a FP16 lookup table. | |
| // The original and float-only results are not equal for all inputs after converting to FP16. | |
| // GELU is an approximation anyway (tanh), not tested here. | |
| // For SILU, verify that the results are at least the closest floating point numbers, if the FP16 values don't match. | |
| for (x = 0; x <= UINT16_MAX; x++) { | |
| float f = _cvtsh_ss(x); | |
| const float so = silu_orig(f); | |
| const float sf = silu_float(f); | |
| assert( (_cvtss_sh(so, 0) == _cvtss_sh(sf, 0)) | |
| || (nextafterf(so, sf) == sf) | |
| || (nextafterf(sf, so) == so)); | |
| } | |
| } | |