drizzymedia

SynapseCoder-32B

README

License: apache-2.0

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

python
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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Model Details

Model Provider

drizzymedia

Model Tree

Base

drizzymedia/SynapseCoder-32B

Fine-tuned

this model

Input Modalities

Text

Output Modalities