Instella-3B-Instruct-abliterated

Instella-3B-Instruct-Abliterated

The Instella models are text-only, autoregressive transformer-based LMs having 3 billion parameters. Architecture-wise, Instella is packed with 36 decoder layers, each having 32 attention heads. These models support a sequence length of up to 4,096 tokens and have a vocabulary size of ~50,000 tokens using the OLMo tokenizer. During both pre-training and fine-tuning, we utilized FlashAttention-2, Torch Compile, and bfloat16 mixed-precision training to reduce memory usage, leading to computational speedups and optimal resource utilization. To balance inter-node memory efficiency and intra-node communication overhead within our cluster, we employed fully sharded data parallelism (FSDP) with hybrid sharding, with model parameters, gradients, and optimizer states sharded within a node and replicated across the nodes.

Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "prithivMLmods/Instella-3B-Instruct-abliterated"

tokenizer = AutoTokenizer.from_pretrained(checkpoint, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(checkpoint, device_map="auto", trust_remote_code=True)

prompt = [{"role": "user", "content": "What are the benefits of open-source AI research?"}]
inputs = tokenizer.apply_chat_template(
    prompt,
    add_generation_prompt=True,
    return_tensors='pt'
)

tokens = model.generate(
    inputs.to(model.device),
    max_new_tokens=1024,
    temperature=0.8,
    do_sample=True
)

print(tokenizer.decode(tokens[0], skip_special_tokens=False))

Overall, Instella-3B-Instruct excels in instruction following tasks and multi-turn QA tasks like TruthfulQA, GPQA, IFEval and MT-Bench, while being highly competitive compared to existing state-of-the-art open weight models on other knowledge recall and math benchmarks, while being trained on significantly fewer training tokens.