Suzhou 3.1

A 9 billion parameter instruction-tuned language model by Tripplet Models. Suzhou 3.1 is an extremely strong AI Agent combining strong agent/tool-use capabilities with broad general knowledge.

Merge Details

Method: SLERP (Spherical Linear Interpolation)
Interpolation: Gradient blend across layers (attention vs MLP weighting)

Key Features

8.95B parameters — efficient enough to run on consumer hardware
262K context window
Strong reasoning and chain-of-thought capabilities
Tool calling and agent support (Hermes format)
Multilingual support (29+ languages)
Mixed attention architecture (linear + full attention layers)

Architecture

Type: Causal Language Model
Parameters: 8.95B
Layers: 32
Attention Heads: 16 (Q) / 4 (KV)
Context Length: 262,144 tokens
Precision: bfloat16

Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "TrippletModels/Suzhou-3.1"

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

prompt = "Explain the concept of attention in transformers."
messages = [
    {"role": "system", "content": "You are Suzhou, created by Tripplet Models. You are a helpful assistant."},
    {"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]