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!
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We encourage others to finetune further from our models.
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Model tree for ValiantLabs/Llama3.1-8B-Cobalt
Base model
meta-llama/Llama-3.1-8BDatasets used to train ValiantLabs/Llama3.1-8B-Cobalt
Collection including ValiantLabs/Llama3.1-8B-Cobalt
Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard71.680
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard27.240
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard15.330
- acc_norm on GPQA (0-shot)Open LLM Leaderboard4.810
- acc_norm on MuSR (0-shot)Open LLM Leaderboard4.700
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard29.590