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
| int main(int argc, char *argv[]) { | |
| int n_threads = 4; | |
| int n_rounds = 100; | |
| if (argc > 1) { | |
| n_threads = std::atoi(argv[1]); | |
| } | |
| if (argc > 2) { | |
| n_rounds = std::atoi(argv[2]); | |
| } | |
| struct ggml_init_params params = { | |
| /* .mem_size = */ 1024*1024*1024, | |
| /* .mem_buffer = */ NULL, | |
| /* .no_alloc = */ false, | |
| }; | |
| struct ggml_context * ctx = ggml_init(params); | |
| // Create graph | |
| struct ggml_cgraph * gf = ggml_new_graph(ctx); | |
| // Lots of small, parallel ops where barriers in between will dominate | |
| struct ggml_tensor * out = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 64); | |
| for (int i = 0; i < 1000; i++) { | |
| struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 64, 128); | |
| out = ggml_mul_mat(ctx, a, out); | |
| struct ggml_tensor * d = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, 128, 64); | |
| out = ggml_mul_mat(ctx, d, out); | |
| } | |
| ggml_build_forward_expand(gf, out); | |
| int n_nodes = ggml_graph_n_nodes(gf); | |
| // Create threadpool | |
| struct ggml_threadpool_params tpp = ggml_threadpool_params_default(n_threads); | |
| struct ggml_threadpool* threadpool = ggml_threadpool_new(&tpp); | |
| if (!threadpool) { | |
| fprintf(stderr, "threadpool create failed : n_threads %d\n", n_threads); | |
| exit(1); | |
| } | |
| // Create compute plan | |
| struct ggml_cplan cplan = ggml_graph_plan(gf, n_threads, threadpool); | |
| std::vector<uint8_t> work_data(cplan.work_size); | |
| cplan.work_data = work_data.data(); | |
| std::cerr << "graph-compute with" | |
| << "\n n_threads: " << n_threads | |
| << "\n n_nodes: " << n_nodes | |
| << "\n n_rounds: " << n_rounds | |
| << "\n"; | |
| // ggml_graph_print(gf); | |
| // Warmup | |
| ggml_graph_compute(gf, &cplan); | |
| auto t0 = std::chrono::high_resolution_clock::now(); | |
| for (int i=0; i < n_rounds; i++) { | |
| ggml_graph_compute(gf, &cplan); | |
| } | |
| auto t1 = std::chrono::high_resolution_clock::now(); | |
| auto usec = std::chrono::duration_cast<std::chrono::microseconds>(t1-t0).count(); | |
| auto nsec = std::chrono::duration_cast<std::chrono::nanoseconds>(t1-t0).count(); | |
| std::cerr << "graph-compute took " << usec << " usec " | |
| << "\n " << (float) usec / n_rounds << " usec per-iter" | |
| << "\n " << (float) nsec / (n_rounds * n_nodes) << " nsec per-node" | |
| << "\n"; | |
| ggml_threadpool_free(threadpool); | |
| ggml_free(ctx); | |
| return 0; | |
| } | |