Instructions to use NorthernTribe-Research/UMSR-Reasoner-7B-INT8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NorthernTribe-Research/UMSR-Reasoner-7B-INT8 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("NorthernTribe-Research/UMSR-Reasoner-7B-INT8", dtype="auto") - Notebooks
- Google Colab
- Kaggle
UMSR-Reasoner-7B (INT8)
8-bit bitsandbytes runtime quantization profile.
Model Tree
- Source model:
https://huggingface.co/NorthernTribe-Research/UMSR-Reasoner-7B - Variant repo:
https://huggingface.co/NorthernTribe-Research/UMSR-Reasoner-7B-INT8 - Dataset:
https://huggingface.co/datasets/NorthernTribe-Research/UMSR-v1
Quantization Profile
- Variant:
int8 - Scheme:
bitsandbytes int8 - Materialized weights in this repo:
False
Usage
This variant repository is a profile card. Load weights from the source model id.
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
model_id = "NorthernTribe-Research/UMSR-Reasoner-7B"
bnb_config = BitsAndBytesConfig(load_in_8bit=True)
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
quantization_config=bnb_config,
)
Output Contract
<final_answer>...</final_answer>
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for NorthernTribe-Research/UMSR-Reasoner-7B-INT8
Base model
NorthernTribe-Research/UMSR-Reasoner-7B