TimeCapsuleLLM-v2-1800-1875-mlx-fp16/README.md

license, language, pipeline_tag, library_name, tags, base_model

license	language	pipeline_tag	library_name	tags	base_model
mit	en	text-generation	transformers	llama historical causal-lm mlx mlx-my-repo	haykgrigorian/TimeCapsuleLLM-v2-1800-1875

FractalSurfer/TimeCapsuleLLM-v2-1800-1875-mlx-fp16

The Model FractalSurfer/TimeCapsuleLLM-v2-1800-1875-mlx-fp16 was converted to MLX format from haykgrigorian/TimeCapsuleLLM-v2-1800-1875 using mlx-lm version 0.29.1.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("FractalSurfer/TimeCapsuleLLM-v2-1800-1875-mlx-fp16")

prompt="hello"

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

response = generate(model, tokenizer, prompt=prompt, verbose=True)