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VertexAGI/amethyst-1-small
amethyst-1-small is a text generation model from VertexAGI. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as llama3.1.
Amethyst 1 Small is a general-purpose chat and instruction-following model, fine-tuned from Llama 3.1 8B Instruct using LoRA on the same distilled instruction dataset used for Amethyst 1 Mini (which is based on Gemma…
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From the Hugging Face model README
Amethyst 1 Small is a general-purpose chat and instruction-following model, fine-tuned from Llama 3.1 8B Instruct using LoRA on the same distilled instruction dataset used for Amethyst 1 Mini (which is based on Gemma 3 4B). It's the second, larger model in the Amethyst family — same data, bigger base model.
| Developed by | Independent research project |
| Base model | meta-llama/Llama-3.1-8B-Instruct |
| Fine-tuning base checkpoint | mlx-community/Meta-Llama-3.1-8B-Instruct-4bit |
| Architecture | Llama 3.1, 8B parameters (dense, decoder-only transformer) |
| Fine-tuning method | LoRA (rank 8, scale 20.0), fused into the base weights — released 4-bit quantized (same quantization as the base checkpoint), not dequantized |
| Fine-tuning framework | MLX / mlx-lm, on Apple Silicon |
| Trained modules | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj across 16 layers |
| Language | English |
| License | Llama 3.1 Community License |
Same 1,122 instruction/response pairs (1,082 train / 40 validation) used for Amethyst 1 Mini — synthetically generated via knowledge distillation from nvidia/nemotron-3-super-120b-a12b (Nemotron-3-Super, a 120B-parameter MoE model, ~12B active) through the OpenRouter API, spanning explanation, reasoning, code, extraction, planning, roleplay, creative writing, translation, sentiment classification, and brainstorming.
Amethyst 1 Small is intended as a general-purpose conversational assistant for experimentation and research into small-scale distillation pipelines. It is not intended for high-stakes, safety-critical, or production use.
from mlx_lm import load, generate
from mlx_lm.sample_utils import make_sampler
model, tokenizer = load("VertexAIco/amethyst-1-small")
messages = [{"role": "user", "content": "Explain how vaccines train the immune system, in simple terms."}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
out = generate(model, tokenizer, prompt=prompt, max_tokens=512, sampler=make_sampler(temp=0.0))
print(out)
@misc{amethyst1small,
title = {Amethyst 1 Small},
author = {Independent research project},
year = {2026},
note = {LoRA fine-tune of Llama 3.1 8B Instruct, distilled from Nemotron-3-Super-120B-A12B}
}
This model is built on Llama 3.1 and subject to the Llama 3.1 Community License.