Text Generation
Transformers
Safetensors
English
qwen3_5_text
weight-space-modeling
zero-gradient
risc-v
in-weights-computer
gated-deltanet
math
graphing-calculator
pedagogy
qwen3_5
conversational
Eval Results (legacy)
Instructions to use DJLougen/Qwen3.5-9B-Math-GraphCalc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DJLougen/Qwen3.5-9B-Math-GraphCalc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DJLougen/Qwen3.5-9B-Math-GraphCalc") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DJLougen/Qwen3.5-9B-Math-GraphCalc") model = AutoModelForCausalLM.from_pretrained("DJLougen/Qwen3.5-9B-Math-GraphCalc", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DJLougen/Qwen3.5-9B-Math-GraphCalc with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DJLougen/Qwen3.5-9B-Math-GraphCalc" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DJLougen/Qwen3.5-9B-Math-GraphCalc", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DJLougen/Qwen3.5-9B-Math-GraphCalc
- SGLang
How to use DJLougen/Qwen3.5-9B-Math-GraphCalc with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "DJLougen/Qwen3.5-9B-Math-GraphCalc" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DJLougen/Qwen3.5-9B-Math-GraphCalc", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "DJLougen/Qwen3.5-9B-Math-GraphCalc" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DJLougen/Qwen3.5-9B-Math-GraphCalc", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use DJLougen/Qwen3.5-9B-Math-GraphCalc with Docker Model Runner:
docker model run hf.co/DJLougen/Qwen3.5-9B-Math-GraphCalc
Download firmware/build.sh from DJLougen/Qwen3.5-9B-Math-GraphCalc: direct link, hf CLI and curl.
- Browser
- Download file 1.58 kB
-
https://huggingface.co/DJLougen/Qwen3.5-9B-Math-GraphCalc/resolve/main/firmware/build.sh
- Command line
-
hf download hf://DJLougen/Qwen3.5-9B-Math-GraphCalc/firmware/build.sh
-
curl -L -o build.sh https://huggingface.co/DJLougen/Qwen3.5-9B-Math-GraphCalc/resolve/main/firmware/build.sh
1.58 kB
| # Build the Carbon Graphing Calculator RV32IM+Zba+Bignum firmware (run in WSL): | |
| # bash carbon/firmware/build.sh [OUT_DIR] | |
| # Produces: | |
| # OUT_DIR/graphcalc.elf (for compiled Qwen3.5 RV32 chip layers & chipformer.rv.ref.Machine) | |
| # OUT_DIR/graphcalc-qemu.elf (for standalone qemu-riscv32 verification via stdin) | |
| set -euo pipefail | |
| HERE=$(cd "$(dirname "$0")" && pwd) | |
| OUT=$(realpath "${1:-$HERE}") | |
| QWENPUTER=${QWENPUTER:-$HERE/../../../qwenputer} | |
| ENV=$QWENPUTER/experimental/riscv/chipformer/rv/env | |
| CALC_ENV=$ENV/calc | |
| GCC=riscv64-unknown-elf-gcc | |
| MARCH=rv32im_zba | |
| PICO=/usr/lib/picolibc/riscv64-unknown-elf | |
| LIBC=$PICO/lib/release/rv32im/ilp32/libc.a | |
| [ -f "$LIBC" ] || LIBC=$(find /usr/lib/picolibc -path '*rv32im/ilp32/libc.a' | head -n 1) | |
| LIBGCC=$($GCC -march=rv32im -mabi=ilp32 -print-libgcc-file-name) | |
| B=$(mktemp -d) | |
| python3 "$HERE/tables.py" "$B/graphcalc_tables.h" "$B/sled.S" | |
| mkdir -p "$OUT" | |
| for map in rom qemu; do | |
| name=$([ $map = rom ] && echo graphcalc || echo graphcalc-qemu) | |
| def=$([ $map = rom ] && echo "" || echo "-DQEMU") | |
| sled=$([ $map = rom ] && echo "$B/sled.S" || echo "") | |
| $GCC -march=$MARCH -mabi=ilp32 ${CFLAGS:--O2} -fno-tree-loop-distribute-patterns -Wall -Wextra $def \ | |
| -isystem "$PICO/include" -I"$ENV" -I"$CALC_ENV" -I"$B" \ | |
| -nostdlib -nostartfiles -static -T"$ENV/$map.ld" -o "$OUT/$name.elf" \ | |
| "$ENV/crt0.S" "$HERE/graphcalc.c" "$ENV/sys.c" $sled \ | |
| -Wl,--start-group "$LIBC" "$LIBGCC" -Wl,--end-group | |
| done | |
| riscv64-unknown-elf-size -A "$OUT/graphcalc.elf" | grep -E '^\.(text|rodata|data|bss)' | |
| rm -rf "$B" | |