Trinity-Nano-Preview-MLX-8bit

<div align="center"> <picture> <img src="https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/i-v1KyAMOW_mgVGeic9WJ.png" alt="Arcee Trinity Mini" style="max-width: 100%; height: auto;" > </picture> </div>

Trinity Nano MLX 8bit

Trinity Nano Preview is a preview of Arcee AI's 6B MoE model with 1B active parameters. It is the small-sized model in our new Trinity family, a series of open-weight models for enterprise and tinkerers alike.

This is a chat tuned model, with a delightful personality and charm we think users will love. We note that this model is pushing the limits of sparsity in small language models with only 800M non-embedding parameters active per token, and as such may be unstable in certain use cases, especially in this preview.

This is an experimental release, it's fun to talk to but will not be hosted anywhere, so download it and try it out yourself!

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Trinity Nano Preview is trained on 10T tokens gathered and curated through a key partnership with Datology, building upon the excellent dataset we used on AFM-4.5B with additional math and code.

Training was performed on a cluster of 512 H200 GPUs powered by Prime Intellect using HSDP parallelism.

More details, including key architecture decisions, can be found on our blog here

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Model Details

• Model Architecture: AfmoeForCausalLM
• Parameters: 6B, 1B active
• Experts: 128 total, 8 active, 1 shared
• Context length: 128k
• Training Tokens: 10T
• License: OpenMDW-1.1

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler, make_logits_processors

model, tokenizer = load("arcee-ai/Trinity-Nano-Preview-MLX-8bit")

prompt = "What is the capital of France?"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, tokenize=False, add_generation_prompt=True
    )

sampler = make_sampler(temp=0.1, top_k=50, top_p=0.1)
logits_processors = make_logits_processors(repetition_penalty=1.05)

response = generate(
    model,
    tokenizer,
    prompt=prompt,
    max_tokens=512,
    sampler=sampler,
    logits_processors=logits_processors,
    verbose=True,
)