Instructions to use maxwelhelp/llama.cpp-DFlash2-pascal6-optimized with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use maxwelhelp/llama.cpp-DFlash2-pascal6-optimized 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 maxwelhelp/llama.cpp-DFlash2-pascal6-optimized # Run inference directly in the terminal: llama cli -hf maxwelhelp/llama.cpp-DFlash2-pascal6-optimized
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf maxwelhelp/llama.cpp-DFlash2-pascal6-optimized # Run inference directly in the terminal: llama cli -hf maxwelhelp/llama.cpp-DFlash2-pascal6-optimized
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 maxwelhelp/llama.cpp-DFlash2-pascal6-optimized # Run inference directly in the terminal: ./llama-cli -hf maxwelhelp/llama.cpp-DFlash2-pascal6-optimized
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 maxwelhelp/llama.cpp-DFlash2-pascal6-optimized # Run inference directly in the terminal: ./build/bin/llama-cli -hf maxwelhelp/llama.cpp-DFlash2-pascal6-optimized
Use Docker
docker model run hf.co/maxwelhelp/llama.cpp-DFlash2-pascal6-optimized
- LM Studio
- Jan
- Ollama
How to use maxwelhelp/llama.cpp-DFlash2-pascal6-optimized with Ollama:
ollama run hf.co/maxwelhelp/llama.cpp-DFlash2-pascal6-optimized
- Unsloth Desktop
- Docker Model Runner
How to use maxwelhelp/llama.cpp-DFlash2-pascal6-optimized with Docker Model Runner:
docker model run hf.co/maxwelhelp/llama.cpp-DFlash2-pascal6-optimized
- Lemonade
How to use maxwelhelp/llama.cpp-DFlash2-pascal6-optimized with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull maxwelhelp/llama.cpp-DFlash2-pascal6-optimized
Run and chat with the model
lemonade run user.llama.cpp-DFlash2-pascal6-optimized-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
| # ============================================================================= | |
| # run_best.sh - DFlash2 + llama.cpp speculative decoding on Tesla P40 (Pascal) | |
| # | |
| # BEST configuration measured across the whole test series (DFLASH2_RING_PORT.md). | |
| # Target: Qwen3.8-27B-UD (Q4_K_XL), Draft: DFlash2 q4-mix self-quant GGUF. | |
| # | |
| # Results (code 1024, reasoning OFF, greedy): | |
| # q4mix draft + DFlash2 + n_max=7 + adaptive margin p_min=0.35 -> 30.26 tok/s | |
| # (acceptance 0.83, mean len 5.25) | |
| # | |
| # Why this is the optimum (see DFLASH2_RING_PORT.md Tests 13-26): | |
| # - q4-mix draft quant: 30.26 > Q8 (~30) > q2h8-hybrid (29) > Q2-all (27). | |
| # - n_max=7 is the sweep optimum: 7=30.26 > 8=30.18 > 5=27.4 > 4=27.8. | |
| # - adaptive margin (p_min=0.35) cuts the chain where the selector is | |
| # uncertain (top1-top2 log-margin), giving short verify batches. | |
| # - P40 hits are: MMQ DP4A (int8, no FP16 tensor cores), -bs backend argmax, | |
| # FA flash-attn, q8_0 KV. | |
| # ============================================================================= | |
| set -euo pipefail | |
| REPO="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" | |
| MODELS=/home/maxwelhelp/models | |
| TARGET="$MODELS/Qwen3.8-27B-UD-Q4_K_XL.gguf" | |
| DRAFT="$MODELS/Qwen3.8-27B-DFlash2-q4mix-self.gguf" | |
| PORT="${PORT:-8080}" | |
| exec "$REPO/build-p40-ring/bin/llama-server" \ | |
| -m "$TARGET" \ | |
| -md "$DRAFT" \ | |
| -ngl 999 -ngld 999 -c 8192 -b 512 -ub 512 -np 1 \ | |
| --load-mode mlock --cache-ram 32768 --checkpoint-min-step 512 \ | |
| -fa 1 -ctk q8_0 -ctv q8_0 -ctkd q8_0 -ctvd q8_0 --kv-unified \ | |
| --spec-type draft-dflash \ | |
| --spec-draft-n-max 7 --spec-draft-n-min 1 --spec-draft-p-min 0.35 \ | |
| --spec-draft-ctx 0 --temp 0 --jinja --reasoning off -bs \ | |
| -lv 4 --host 0.0.0.0 --port "$PORT" | |