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
| import time | |
| import argparse | |
| from transformers import AutoTokenizer | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file") | |
| parser.add_argument("--fname-tok", help="path to a text file to tokenize", required=True) | |
| args = parser.parse_args() | |
| dir_tokenizer = args.dir_tokenizer | |
| fname_tok = args.fname_tok | |
| tokenizer = AutoTokenizer.from_pretrained(dir_tokenizer) | |
| print('tokenizing file: ', fname_tok) # noqa: NP100 | |
| fname_out = fname_tok + '.tok' | |
| with open(fname_tok, 'r', encoding='utf-8') as f: | |
| lines = f.readlines() | |
| s = ''.join(lines) | |
| t_start = time.time() | |
| res = tokenizer.encode(s, add_special_tokens=False) | |
| t_end = time.time() | |
| print('\nmain : tokenized in', "{:.3f}".format(1000.0 * (t_end - t_start)), 'ms (py)') # noqa: NP100 | |
| with open(fname_out, 'w', encoding='utf-8') as f: | |
| for x in res: | |
| # LLaMA v3 for some reason strips the space for these tokens (and others) | |
| # if x == 662: | |
| # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') | |
| # elif x == 1174: | |
| # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') | |
| # elif x == 2564: | |
| # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') | |
| # elif x == 758: | |
| # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') | |
| # elif x == 949: | |
| # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') | |
| # elif x == 5354: | |
| # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') | |
| # else: | |
| # f.write(str(x) + ' \'' + tokenizer.decode(x) + '\'\n') | |
| # f.write(str(x) + ' \'' + tokenizer.decode(x).strip() + '\'\n') | |
| f.write(str(x) + '\n') | |
| print('len(res): ', len(res)) # noqa: NP100 | |
| print('len(lines): ', len(lines)) # noqa: NP100 | |
| print('results written to: ', fname_out) # noqa: NP100 | |