README.md

metadata

base_model: pints-ai/1.5-Pints-16K-v0.1
datasets:
  - pints-ai/Expository-Prose-V1
  - HuggingFaceH4/ultrachat_200k
  - Open-Orca/SlimOrca-Dedup
  - meta-math/MetaMathQA
  - HuggingFaceH4/deita-10k-v0-sft
  - WizardLM/WizardLM_evol_instruct_V2_196k
  - togethercomputer/llama-instruct
  - LDJnr/Capybara
  - HuggingFaceH4/ultrafeedback_binarized
language:
  - en
license: mit
pipeline_tag: text-generation
tags:
  - mlx
extra_gated_prompt: >-
  Though best efforts has been made to ensure, as much as possible, that all
  texts in the training corpora are royalty free, this does not constitute a
  legal guarantee that such is the case. **By using any of the models, corpora
  or part thereof, the user agrees to bear full responsibility to do the
  necessary due diligence to ensure that he / she is in compliance with their
  local copyright laws. Additionally, the user agrees to bear any damages
  arising as a direct cause (or otherwise) of using any artifacts released by
  the pints research team, as well as full responsibility for the consequences
  of his / her usage (or implementation) of any such released artifacts. The
  user also indemnifies Pints Research Team (and any of its members or agents)
  of any damage, related or unrelated, to the release or subsequent usage of any
  findings, artifacts or code by the team. For the avoidance of doubt, any
  artifacts released by the Pints Research team are done so in accordance with
  the 'fair use' clause of Copyright Law, in hopes that this will aid the
  research community in bringing LLMs to the next frontier.
extra_gated_fields:
  Company: text
  Country: country
  Specific date: date_picker
  I want to use this model for:
    type: select
    options:
      - Research
      - Education
      - label: Other
        value: other
  I agree to use this model for in accordance to the afore-mentioned Terms of Use: checkbox
model-index:
  - name: 1.5-Pints
    results:
      - task:
          type: text-generation
        dataset:
          name: MTBench
          type: ai2_arc
        metrics:
          - type: LLM-as-a-Judge
            value: 3.4
            name: MTBench
        source:
          url: https://huggingface.co/spaces/lmsys/mt-bench
          name: MTBench

mlx-community/1.5-Pints-16K-v0.1

The Model mlx-community/1.5-Pints-16K-v0.1 was converted to MLX format from pints-ai/1.5-Pints-16K-v0.1 using mlx-lm version 0.19.2.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/1.5-Pints-16K-v0.1")

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)