Instructions to use mlx-community/1.5-Pints-16K-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/1.5-Pints-16K-v0.1 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("mlx-community/1.5-Pints-16K-v0.1") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use mlx-community/1.5-Pints-16K-v0.1 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "mlx-community/1.5-Pints-16K-v0.1"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "mlx-community/1.5-Pints-16K-v0.1" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mlx-community/1.5-Pints-16K-v0.1", "messages": [ {"role": "user", "content": "Hello"} ] }' - Atomic Chat
| 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](https://huggingface.co/mlx-community/1.5-Pints-16K-v0.1) was converted to MLX format from [pints-ai/1.5-Pints-16K-v0.1](https://huggingface.co/pints-ai/1.5-Pints-16K-v0.1) using mlx-lm version **0.19.2**. | |
| ## Use with mlx | |
| ```bash | |
| pip install mlx-lm | |
| ``` | |
| ```python | |
| 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) | |
| ``` | |