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
| set -e | |
| # Array of models to iterate over | |
| declare -a params=( | |
| "Gemma2ForCausalLM 64" | |
| "LlamaForCausalLM 64" | |
| "Phi3ForCausalLM 64" | |
| ) | |
| MODELS_REPO=lora-tests | |
| MODELS_REPO_URL=https://huggingface.co/ggml-org/$MODELS_REPO | |
| # Clone the Hugging Face repository if the directory does not exist | |
| if [ ! -d "$MODELS_REPO" ]; then | |
| echo "Cloning the Hugging Face repository..." | |
| git clone $MODELS_REPO_URL --depth 1 | |
| else | |
| echo "Repository already exists. Skipping clone." | |
| fi | |
| # Array to store results to print | |
| results=() | |
| trim_leading_whitespace() { | |
| local input_string="$1" | |
| echo "${input_string#"${input_string%%[![:space:]]*}"}" | |
| } | |
| extract_starting_substring() { | |
| local reference_string="$1" | |
| local target_string="$2" | |
| local target_length=${#target_string} | |
| echo "${reference_string:0:$target_length}" | |
| } | |
| get_first_word() { | |
| local input_string="$1" | |
| read -r first_word _ <<< "$input_string" | |
| echo "$first_word" | |
| } | |
| # Load the expected strings | |
| EXPECTED_BASE_FULL=$(cat $MODELS_REPO/data/pale_blue_dot.txt) | |
| EXPECTED_LORA_FULL=$(cat $MODELS_REPO/data/bohemian_rhapsody.txt) | |
| EXPECTED_BASE_FIRST_WORD=$(get_first_word "$EXPECTED_BASE_FULL") | |
| EXPECTED_LORA_FIRST_WORD=$(get_first_word "$EXPECTED_LORA_FULL") | |
| run_conversion_and_inference_lora() { | |
| local model_name=$1 | |
| local hidden_size=$2 | |
| echo -e "\n\n-------- RUNNING TEST FOR MODEL $model_name --------\n\n" | |
| # Convert safetensors to gguf | |
| echo "Running convert_hf_to_gguf.py for $model_name with hidden_size $hidden_size..." | |
| python convert_hf_to_gguf.py $MODELS_REPO/$model_name/hidden_size=$hidden_size/base \ | |
| --outfile $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32.gguf \ | |
| --outtype f32 | |
| echo -e "\n\n---------------------------\n\n" | |
| echo "Running convert_lora_to_gguf.py for $model_name with hidden_size $hidden_size..." | |
| python3 convert_lora_to_gguf.py $MODELS_REPO/$model_name/hidden_size=$hidden_size/lora \ | |
| --base $MODELS_REPO/$model_name/hidden_size=$hidden_size/base \ | |
| --outtype f32 | |
| echo -e "\n\n---------------------------\n\n" | |
| echo "Running llama-export-lora with lora for $model_name with hidden_size $hidden_size..." | |
| ./llama-export-lora \ | |
| -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32.gguf \ | |
| -o $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32-lora-merged.gguf \ | |
| --lora $MODELS_REPO/$model_name/hidden_size=$hidden_size/lora/Lora-F32-LoRA.gguf | |
| # Run inference | |
| echo -e "\n\n---------------------------\n\n" | |
| echo "Running llama-cli without lora for $model_name with hidden_size $hidden_size..." | |
| OUTPUT_BASE=$(./llama-cli -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32.gguf \ | |
| -p "$EXPECTED_BASE_FIRST_WORD" -n 50 --seed 42 --temp 0) | |
| echo -e "\n\n---------------------------\n\n" | |
| echo "Running llama-cli with hot lora for $model_name with hidden_size $hidden_size..." | |
| OUTPUT_LORA_HOT=$(./llama-cli -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32.gguf \ | |
| --lora $MODELS_REPO/$model_name/hidden_size=$hidden_size/lora/Lora-F32-LoRA.gguf \ | |
| -p "$EXPECTED_LORA_FIRST_WORD" -n 50 --seed 42 --temp 0) | |
| echo -e "\n\n---------------------------\n\n" | |
| echo "Running llama-cli with merged lora for $model_name with hidden_size $hidden_size..." | |
| OUTPUT_LORA_MERGED=$(./llama-cli -m $MODELS_REPO/$model_name/hidden_size=$hidden_size/base/Base-F32-lora-merged.gguf \ | |
| -p "$EXPECTED_LORA_FIRST_WORD" -n 50 --seed 42 --temp 0) | |
| # Remove any initial white space | |
| OUTPUT_BASE=$(trim_leading_whitespace "$OUTPUT_BASE") | |
| OUTPUT_LORA_HOT=$(trim_leading_whitespace "$OUTPUT_LORA_HOT") | |
| OUTPUT_LORA_MERGED=$(trim_leading_whitespace "$OUTPUT_LORA_MERGED") | |
| # Extract the corresponding substring from full string | |
| EXPECTED_BASE=$(extract_starting_substring "$EXPECTED_BASE_FULL" "$OUTPUT_BASE") | |
| EXPECTED_LORA=$(extract_starting_substring "$EXPECTED_LORA_FULL" "$OUTPUT_LORA_HOT") | |
| # Assert output equals the expected output | |
| if [[ "$OUTPUT_BASE" != "$EXPECTED_BASE" ]]; then | |
| echo "Error: $model_name OUTPUT_BASE does not start with the expected string." | |
| echo -e "Out=$OUTPUT_BASE\n\nExp=$EXPECTED_BASE" | |
| exit 1 | |
| fi | |
| if [[ "$OUTPUT_LORA_HOT" != "$EXPECTED_LORA" ]]; then | |
| echo "Error: $model_name OUTPUT_LORA_HOT does not start with the expected string." | |
| echo -e "Out=$OUTPUT_LORA_HOT\n\nExp=$EXPECTED_LORA" | |
| exit 1 | |
| fi | |
| if [[ "$OUTPUT_LORA_MERGED" != "$EXPECTED_LORA" ]]; then | |
| echo "Error: $model_name OUTPUT_LORA_MERGED does not start with the expected string." | |
| echo -e "Out=$OUTPUT_LORA_MERGED\n\nExp=$EXPECTED_LORA" | |
| exit 1 | |
| fi | |
| # Store the results | |
| results+=(" | |
| \n\033[1mResults for $model_name with hidden_size $hidden_size:\033[0m | |
| \n\033[32m • Base:\n$OUTPUT_BASE | |
| \n\033[34m • Lora hot:\n$OUTPUT_LORA_HOT | |
| \n\033[36m • Lora merged:\n$OUTPUT_LORA_MERGED | |
| \n \033[0m | |
| ") | |
| echo "All tests passed for $model_name with hidden_size $hidden_size!" | |
| } | |
| # Run test for each model | |
| for param in "${params[@]}"; do | |
| run_conversion_and_inference_lora $param | |
| done | |
| # Print results | |
| echo -e "\n\n---------------------------\n\n" | |
| echo -e "\n\033[1mSummary of All Results:\033[0m" | |
| for result in "${results[@]}"; do | |
| echo -e "$result" | |
| done | |