README.md

metadata

license: apache-2.0
language:
  - en
  - multilingual
  - de
  - fr
  - es
  - zh
  - jp
tags:
  - merge
  - uncensored
  - unrestricted
  - reasoning
  - tool-use
  - multimodal
  - vision
  - long-context
  - transformers
  - conversational
  - instruction-following
  - zero-shot
  - few-shot
  - code-generation
  - text-generation
  - summarization
  - question-answering
  - multi-task
  - dialogue
datasets:
  - openhermes-2.5
  - ultrachat
  - glaive-tool-call
  - laion/OIG
  - pubmed-qa
  - stack-exchange-preferences-10k
  - mmlu
  - gsm8k
  - openwebtext
  - pile
metrics:
  - mt-bench
  - arena-hard
  - mmlu-pro
  - perplexity
  - rouge
  - bleu
  - accuracy
  - f1
  - hits-at-1
  - hits-at-5
base_model:
  - cognitivecomputations/dolphin-2.9.3-yi-1.5-34b
  - llava-hf/llava-v1.6-mistral-7b-hf
  - firefly-llm/firefly-13b-tool
  - google/flan-t5-xl
  - facebook/opt-6.7b
  - huggingface/gpt2
pipeline_tag: text-generation
library_name: transformers
inference: true
architecture:
  - LlamaForCausalLM
  - MistralForCausalLM
  - GPT2LMHeadModel
tags_extended:
  - arithmetic
  - logical-reasoning
  - commonsense-reasoning
  - natural-language-understanding
  - multi-step-reasoning
  - summarization
  - classification
  - code-completion
  - translation
  - multimodal-vision
  - tool-integration
capabilities:
  - unrestricted-generation
  - zero-shot
  - few-shot
  - multi-turn-dialogue
  - instruction-following
  - reasoning
  - code-assistance
  - knowledge-retrieval
  - summarization
  - long-context-handling
  - multimodal-processing
limitations:
  - may require large GPU for optimal performance
  - some tasks may need fine-tuning
  - performance depends on input prompt clarity
  - model outputs are only as reliable as the training data
references:
  - https://huggingface.co/docs
  - https://huggingface.co/transformers/
  - https://arxiv.org/abs/2005.14165
  - https://arxiv.org/abs/2306.11343
  - https://arxiv.org/abs/2307.00262
evaluation:
  - mmlu
  - gsm8k
  - hellaswag
  - openai-evals
  - factuality-tests
  - robustness-tests

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)