Shay

Shay is a merged large language model integrating advanced reasoning, tool-use, and multimodal capabilities.
It is designed for unrestricted, unfiltered, unbiased operation with support for long-context processing, multi-step reasoning, and instruction-following tasks.

Shay can perform text generation, summarization, code assistance, translation, and more.

Merge Details

• Merge method: task_arithmetic
• Density: 0.71
• Weight: 0.55
• Normalization: enabled
• INT8 masking: enabled
• Dtype: bfloat16
• Max context tokens supported: 40k
• Max generation tokens recommended: 512

Usage Example

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "your-username/Shay"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    attn_implementation="flash_attention_2",
    trust_remote_code=True,
    rope_scaling={"type": "dynamic", "factor": 10.0}
)

# Safe example prompt
prompt = """<|system|>
You are an intelligent, helpful assistant.
<|user|>
Write a detailed plan for organizing a community event with volunteers, budget, and timeline.
<|assistant|>
"""

# Prepare inputs
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

# Generate output
output = model.generate(
    **inputs,
    max_new_tokens=512,
    temperature=1.05,
    top_p=0.97,
    top_k=60,
    repetition_penalty=1.12,
    do_sample=True
)

# Decode the response
reply = tokenizer.decode(output[0], skip_special_tokens=True)
reply = reply.split("<|assistant|>")[-1].strip()

print(reply)