Instructions to use DJLougen/Qwen3.5-9B-Math-GraphCalc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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
```

Qwen3.5-9B-Math-GraphCalc / firmware /tables.py

DJLougen

Upload Qwen3.5-9B-Math-GraphCalc (43-layer zero-gradient WSP + in-weights RV32 graphing calculator)

9b8659b verified 1 day ago

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2.46 kB

"""Generate DROM fixed-point math lookup tables (`graphcalc_tables.h`) and bignum print sleds (`sled.S`)."""
import math
import sys
from pathlib import Path


def write_tables(hdr_path: Path) -> None:
    one32 = 1 << 32
    lines = [
        "// Auto-generated Q32.32 constants and CORDIC / log tables in DROM (.rodata).",
        "#pragma once",
        "#include <stdint.h>",
        "",
        f"#define Q32_ONE ({one32}LL)",
        f"#define Q32_PI ({round(math.pi * one32)}LL)",
        f"#define Q32_TWO_PI ({round(2.0 * math.pi * one32)}LL)",
        f"#define Q32_INV_TWO_PI ({round((1.0 / (2.0 * math.pi)) * one32)}LL)",
        f"#define Q32_HALF_PI ({round(0.5 * math.pi * one32)}LL)",
        f"#define Q32_E ({round(math.e * one32)}LL)",
        f"#define Q32_LN2 ({round(math.log(2.0) * one32)}LL)",
        f"#define Q32_INV_LN2 ({round((1.0 / math.log(2.0)) * one32)}LL)",
        f"#define Q32_LN10 ({round(math.log(10.0) * one32)}LL)",
        "",
    ]
    # CORDIC gain K = prod_{i=0}^{29} 1 / sqrt(1 + 2^{-2i})
    k_gain = 1.0
    atan_vals = []
    for i in range(30):
        k_gain /= math.hypot(1.0, 2.0 ** (-i))
        atan_vals.append(round(math.atan(2.0 ** (-i)) * one32))
    lines.append(f"#define CORDIC_K ({round(k_gain * one32)}LL)")
    lines.append("static const int64_t CORDIC_ATAN[30] = {")
    for i in range(0, 30, 5):
        chunk = ", ".join(f"{v}LL" for v in atan_vals[i : i + 5])
        lines.append(f"    {chunk},")
    lines.append("};\n")

    # Shifted log table: ln(1 + 2^{-i}) for i = 1..30 in Q32.32
    ln1p_vals = [0] + [round(math.log1p(2.0 ** (-i)) * one32) for i in range(1, 31)]
    lines.append("static const int64_t LN1P_POW2[31] = {")
    for i in range(0, 31, 5):
        chunk = ", ".join(f"{v}LL" for v in ln1p_vals[i : i + 5])
        lines.append(f"    {chunk},")
    lines.append("};\n")

    hdr_path.write_text("\n".join(lines), encoding="utf-8")


def write_sled(sled_path: Path) -> None:
    out = ["    .section .text", "    .globl bn_sleds"]
    for r in range(8):
        out += [f"bn_sled{r}:"] + [f"    .insn i 0x2B, 0, x{r}, x0, {k}" for k in range(127, -1, -1)]
        out += ["    ret"]
    out += ["    .section .rodata", "    .align 2", "bn_sleds:"]
    out += [f"    .word bn_sled{r}" for r in range(8)]
    sled_path.write_text("\n".join(out) + "\n", encoding="utf-8")


if __name__ == "__main__":
    write_tables(Path(sys.argv[1]))
    write_sled(Path(sys.argv[2]))

	"""Generate DROM fixed-point math lookup tables (`graphcalc_tables.h`) and bignum print sleds (`sled.S`)."""
	import math
	import sys
	from pathlib import Path


	def write_tables(hdr_path: Path) -> None:
	one32 = 1 << 32
	lines = [
	"// Auto-generated Q32.32 constants and CORDIC / log tables in DROM (.rodata).",
	"#pragma once",
	"#include <stdint.h>",
	"",
	f"#define Q32_ONE ({one32}LL)",
	f"#define Q32_PI ({round(math.pi * one32)}LL)",
	f"#define Q32_TWO_PI ({round(2.0 * math.pi * one32)}LL)",
	f"#define Q32_INV_TWO_PI ({round((1.0 / (2.0 * math.pi)) * one32)}LL)",
	f"#define Q32_HALF_PI ({round(0.5 * math.pi * one32)}LL)",
	f"#define Q32_E ({round(math.e * one32)}LL)",
	f"#define Q32_LN2 ({round(math.log(2.0) * one32)}LL)",
	f"#define Q32_INV_LN2 ({round((1.0 / math.log(2.0)) * one32)}LL)",
	f"#define Q32_LN10 ({round(math.log(10.0) * one32)}LL)",
	"",
	]
	# CORDIC gain K = prod_{i=0}^{29} 1 / sqrt(1 + 2^{-2i})
	k_gain = 1.0
	atan_vals = []
	for i in range(30):
	k_gain /= math.hypot(1.0, 2.0 ** (-i))
	atan_vals.append(round(math.atan(2.0 ** (-i)) * one32))
	lines.append(f"#define CORDIC_K ({round(k_gain * one32)}LL)")
	lines.append("static const int64_t CORDIC_ATAN[30] = {")
	for i in range(0, 30, 5):
	chunk = ", ".join(f"{v}LL" for v in atan_vals[i : i + 5])
	lines.append(f" {chunk},")
	lines.append("};\n")

	# Shifted log table: ln(1 + 2^{-i}) for i = 1..30 in Q32.32
	ln1p_vals = [0] + [round(math.log1p(2.0 ** (-i)) * one32) for i in range(1, 31)]
	lines.append("static const int64_t LN1P_POW2[31] = {")
	for i in range(0, 31, 5):
	chunk = ", ".join(f"{v}LL" for v in ln1p_vals[i : i + 5])
	lines.append(f" {chunk},")
	lines.append("};\n")

	hdr_path.write_text("\n".join(lines), encoding="utf-8")


	def write_sled(sled_path: Path) -> None:
	out = [" .section .text", " .globl bn_sleds"]
	for r in range(8):
	out += [f"bn_sled{r}:"] + [f" .insn i 0x2B, 0, x{r}, x0, {k}" for k in range(127, -1, -1)]
	out += [" ret"]
	out += [" .section .rodata", " .align 2", "bn_sleds:"]
	out += [f" .word bn_sled{r}" for r in range(8)]
	sled_path.write_text("\n".join(out) + "\n", encoding="utf-8")


	if __name__ == "__main__":
	write_tables(Path(sys.argv[1]))
	write_sled(Path(sys.argv[2]))