You can deploy this model and start earning money today!

QuantFactory/
Llama3.1-8B-Cobalt-GGUF

Name: Llama3.1-8B-Cobalt-GGUF
Availability: InStock

Views

No views yet

QuantFactory/Llama3.1-8B-Cobalt-GGUF

This is quantized version of ValiantLabs/Llama3.1-8B-Cobalt created using llama.cpp

Original Model Card

Cobalt is a math-instruct model built on Llama 3.1 8b.

High quality math instruct performance within the Llama 3 Instruct chat format
Finetuned on synthetic math-instruct data generated with Llama 3.1 405b. Find the current version of the dataset here!

Version

This is the 2024-08-16 release of Cobalt for Llama 3.1 8b.

Help us and recommend Cobalt to your friends! We're excited for more Cobalt releases in the future.

Right now, we're working on more new Build Tools to come very soon, built on Llama 3.1 :)

Prompting Guide

Cobalt uses the Llama 3.1 Instruct prompt format. The example script below can be used as a starting point for general chat:

import transformers import torch

model_id = "ValiantLabs/Llama3.1-8B-Cobalt"

pipeline = transformers.pipeline( "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto", )

messages = [ {"role": "system", "content": "You are Cobalt, expert math AI."}, {"role": "user", "content": "I'm buying a $50 shirt and a $80 pair of pants, both currently at a 25% discount. How much will I pay?"} ]

outputs = pipeline( messages, max_new_tokens=1024, )

print(outputs[0]["generated_text"][-1])

The Model

Cobalt is built on top of Llama 3.1 8b Instruct, using math-instruct data to supplement math-instruct performance using Llama 3.1 Instruct prompt style.

Our current version of the Cobalt math-instruct dataset is sequelbox/Polytope, supplemented with a small selection of data from LDJnr/Pure-Dove for general chat consistency.

Cobalt is created by Valiant Labs.

Check out our HuggingFace page for Shining Valiant 2 and our other Build Tools models for creators!

We care about open source. For everyone to use.

We encourage others to finetune further from our models.