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drizzymedia
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Model Details
Model Provider
drizzymedia
Model Tree
Qwen/Qwen2.5-32B
Input Modalities
Output Modalities
Supported Functionality
This model uses gated access.
To use this model:
This model supports LoRA and QLoRA fine-tuning.
Example:
python
from transformers import AutoModelForCausalLMmodel = AutoModelForCausalLM.from_pretrained("drizzymedia/SynapseR-32B")# SynapseR-32B<a href="https://synapse.ai/" target="_blank" style="margin: 2px;"><img alt="Synapse Chat" src="https://img.shields.io/badge/%F0%9F%A7%A0%20Synapse%20Chat-6B4EFF" style="display: inline-block; vertical-align: middle;"/></a>## IntroductionSynapseR is the advanced reasoning model within the Synapse AI series. Compared with conventional instruction-tuned models, SynapseR is designed with enhanced thinking and reasoning capabilities, delivering significantly improved performance on complex downstream tasks, especially advanced problem-solving, mathematics, coding, and analytical workloads.SynapseR-32B is the medium-sized reasoning model in the SynapseR family, capable of competing with leading reasoning systems while providing a powerful balance between intelligence, efficiency, and deployment flexibility.<p align="center"><img width="100%" src="figures/benchmark.jpg"></p>**This repository contains the SynapseR-32B model**, featuring:- Type: Causal Language Model- Training Stage: Pretraining & Post-training (Supervised Fine-Tuning and Reinforcement Learning)- Architecture: Transformer architecture 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- For prompts exceeding 8,192 tokens, enable Synapse Long Context Scaling for improved long-context understanding.**Note:** For the best experience, review the SynapseR deployment guidelines before running production workloads.You can try the SynapseR demo or access Synapse models through Synapse Chat.For more details, visit the official Synapse AI documentation, GitHub repository, and model resources.## RequirementsSynapseR is built on modern Transformer architecture and requires an up-to-date version of the Hugging Face `transformers` library.Older versions may cause compatibility issues during model loading.## QuickstartExample usage with Transformers:```pythonfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_name = "drizzymedia/SynapseR-32B"model = AutoModelForCausalLM.from_pretrained(model_name,torch_dtype="auto",device_map="auto")tokenizer = AutoTokenizer.from_pretrained(model_name)prompt = "Explain how neural networks learn step by step."messages = [{"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=32768,temperature=0.6,top_p=0.95)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)