RichardErkhov/ibm_-_PowerLM-3b-gguf

Quantization made by Richard Erkhov.

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PowerLM-3b - GGUF

• Model creator: https://huggingface.co/ibm/
• Original model: https://huggingface.co/ibm/PowerLM-3b/

Name	Quant method	Size
PowerLM-3b.Q2_K.gguf	Q2_K	1.25GB
PowerLM-3b.IQ3_XS.gguf	IQ3_XS	1.38GB
PowerLM-3b.IQ3_S.gguf	IQ3_S	1.45GB
PowerLM-3b.Q3_K_S.gguf	Q3_K_S	1.45GB
PowerLM-3b.IQ3_M.gguf	IQ3_M	1.52GB
PowerLM-3b.Q3_K.gguf	Q3_K	1.62GB
PowerLM-3b.Q3_K_M.gguf	Q3_K_M	1.62GB
PowerLM-3b.Q3_K_L.gguf	Q3_K_L	1.76GB
PowerLM-3b.IQ4_XS.gguf	IQ4_XS	1.79GB
PowerLM-3b.Q4_0.gguf	Q4_0	1.87GB
PowerLM-3b.IQ4_NL.gguf	IQ4_NL	1.89GB
PowerLM-3b.Q4_K_S.gguf	Q4_K_S	1.89GB
PowerLM-3b.Q4_K.gguf	Q4_K	2.0GB
PowerLM-3b.Q4_K_M.gguf	Q4_K_M	2.0GB
PowerLM-3b.Q4_1.gguf	Q4_1	2.07GB
PowerLM-3b.Q5_0.gguf	Q5_0	2.27GB
PowerLM-3b.Q5_K_S.gguf	Q5_K_S	2.27GB
PowerLM-3b.Q5_K.gguf	Q5_K	2.33GB
PowerLM-3b.Q5_K_M.gguf	Q5_K_M	2.33GB
PowerLM-3b.Q5_1.gguf	Q5_1	2.47GB
PowerLM-3b.Q6_K.gguf	Q6_K	2.69GB
PowerLM-3b.Q8_0.gguf	Q8_0	3.48GB

Original model description:

pipeline_tag: text-generation inference: false license: apache-2.0 library_name: transformers model-index: - name: ibm/PowerLM-3b results: - task: type: text-generation dataset: type: lm-eval-harness name: ARC metrics: - name: accuracy-norm type: accuracy-norm value: 60.5 verified: false - task: type: text-generation dataset: type: lm-eval-harness name: BoolQ metrics: - name: accuracy type: accuracy value: 72.0 verified: false - task: type: text-generation dataset: type: lm-eval-harness name: Hellaswag metrics: - name: accuracy-norm type: accuracy-norm value: 74.6 verified: false - task: type: text-generation dataset: type: lm-eval-harness name: OpenBookQA metrics: - name: accuracy-norm type: accuracy-norm value: 43.6 verified: false - task: type: text-generation dataset: type: lm-eval-harness name: PIQA metrics: - name: accuracy-norm type: accuracy-norm value: 79.9 verified: false - task: type: text-generation dataset: type: lm-eval-harness name: Winogrande metrics: - name: accuracy-norm type: accuracy-norm value: 70.0 verified: false - task: type: text-generation dataset: type: lm-eval-harness name: MMLU (5 shot) metrics: - name: accuracy type: accuracy value: 49.2 verified: false - task: type: text-generation dataset: type: lm-eval-harness name: GSM8k (5 shot) metrics: - name: accuracy type: accuracy value: 34.9 verified: false - task: type: text-generation dataset: type: lm-eval-harness name: math (4 shot) metrics: - name: accuracy type: accuracy value: 15.2 verified: false - task: type: text-generation dataset: type: bigcode-eval name: humaneval metrics: - name: pass@1 type: pass@1 value: 26.8 verified: false - task: type: text-generation dataset: type: bigcode-eval name: MBPP metrics: - name: pass@1 type: pass@1 value: 33.6 verified: false

Model Summary

PowerLM-3B is a 3B state-of-the-art small language model trained with the Power learning rate scheduler. It is trained on a mix of open-source and proprietary datasets. PowerLM-3B has shown promising results compared to other models in the size categories across various benchmarks, including natural language multi-choices, code generation, and math reasoning. Paper: https://arxiv.org/abs/2408.13359

Usage

Note: Requires installing HF transformers from source.

Generation

This is a simple example of how to use PowerLM-3b model.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # or "cpu"
model_path = "ibm/PowerLM-3b"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()
# change input text as desired
prompt = "Write a code to find the maximum value in a list of numbers."
# tokenize the text
input_tokens = tokenizer(prompt, return_tensors="pt")
# transfer tokenized inputs to the device
for i in input_tokens:
    input_tokens[i] = input_tokens[i].to(device)
# generate output tokens
output = model.generate(**input_tokens, max_new_tokens=100)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# loop over the batch to print, in this example the batch size is 1
for i in output:
    print(i)

Additional thanks to @nicoboss for giving me access to his private supercomputer, enabling me to provide many more quants, at much higher speed, than I would otherwise be able to.