Instructions to use drizzymedia/SynapseCoder-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use drizzymedia/SynapseCoder-32B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="drizzymedia/SynapseCoder-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("drizzymedia/SynapseCoder-32B")
model = AutoModelForCausalLM.from_pretrained("drizzymedia/SynapseCoder-32B", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use drizzymedia/SynapseCoder-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "drizzymedia/SynapseCoder-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "drizzymedia/SynapseCoder-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/drizzymedia/SynapseCoder-32B

SGLang

How to use drizzymedia/SynapseCoder-32B 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 "drizzymedia/SynapseCoder-32B" \
    --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": "drizzymedia/SynapseCoder-32B",
		"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 "drizzymedia/SynapseCoder-32B" \
        --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": "drizzymedia/SynapseCoder-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use drizzymedia/SynapseCoder-32B with Docker Model Runner:
```
docker model run hf.co/drizzymedia/SynapseCoder-32B
```

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SynapseCoder-32B

Introduction

SynapseCoder is the advanced code-specialized language model series from Synapse AI. Built for modern software development, SynapseCoder provides powerful capabilities across code generation, code reasoning, debugging, refactoring, and AI-powered development workflows.

SynapseCoder brings significant improvements in:

Code generation, code understanding, and code fixing
Software engineering reasoning for real-world development tasks
AI coding agents and autonomous developer workflows
Long-context programming support for large codebases and complex projects

SynapseCoder-32B is the flagship coding model in the SynapseCoder family, designed to deliver professional-level programming assistance while maintaining strong general reasoning and mathematical capabilities.

This repository contains the instruction-tuned 32B SynapseCoder model, featuring:

Type: Causal Language Model
Training Stage: Pretraining & Post-training
Architecture: Transformer with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
Number of Parameters: 32.5B
Number of Non-Embedding Parameters: 31.0B
Number of Layers: 64
Number of Attention Heads (GQA): 40 for Query and 8 for Key/Value
Context Length: Up to 131,072 tokens

SynapseCoder is optimized for:

Software development
Code generation
Code completion
Debugging
Refactoring
Documentation generation
AI coding assistants
Autonomous coding agents

Requirements

SynapseCoder requires the latest version of Hugging Face transformers.

Older versions may cause compatibility issues during model loading.

Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "drizzymedia/SynapseCoder-32B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Write a Python quick sort algorithm."

messages = [
    {
        "role": "system",
        "content": "You are SynapseCoder, an advanced AI coding assistant created by Synapse AI."
    },
    {
        "role": "user",
        "content": prompt
    }
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer(
    [text],
    return_tensors="pt"
).to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)

generated_ids = [
    output_ids[len(input_ids):]
    for input_ids, output_ids in zip(
        model_inputs.input_ids,
        generated_ids
    )
]

response = tokenizer.batch_decode(
    generated_ids,
    skip_special_tokens=True
)[0]

print(response)

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Safetensors

Model size

33B params

Tensor type

BF16

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