---
license: mit
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
pipeline_tag: text-generation
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
base_model: pints-ai/1.5-Pints-16K-v0.1
tags:
- llama-cpp
- gguf-my-repo
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
---

# Felladrin/1.5-Pints-16K-v0.1-Q4_K_S-GGUF
This model was converted to GGUF format from [`pints-ai/1.5-Pints-16K-v0.1`](https://huggingface.co/pints-ai/1.5-Pints-16K-v0.1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/pints-ai/1.5-Pints-16K-v0.1) for more details on the model.

## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Felladrin/1.5-Pints-16K-v0.1-Q4_K_S-GGUF --hf-file 1.5-pints-16k-v0.1-q4_k_s.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Felladrin/1.5-Pints-16K-v0.1-Q4_K_S-GGUF --hf-file 1.5-pints-16k-v0.1-q4_k_s.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Felladrin/1.5-Pints-16K-v0.1-Q4_K_S-GGUF --hf-file 1.5-pints-16k-v0.1-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Felladrin/1.5-Pints-16K-v0.1-Q4_K_S-GGUF --hf-file 1.5-pints-16k-v0.1-q4_k_s.gguf -c 2048
```