language:
- en
pipeline_tag: text-generation
tags:
- esper
- esper-2
- valiant
- valiant-labs
- llama
- llama-3.2
- llama-3.2-instruct
- llama-3.2-instruct-3b
- llama-3
- llama-3-instruct
- llama-3-instruct-3b
- 3b
- code
- code-instruct
- python
- dev-ops
- terraform
- azure
- aws
- gcp
- architect
- engineer
- developer
- conversational
- chat
- instruct
base_model: meta-llama/Llama-3.2-3B-Instruct
datasets:
- sequelbox/Titanium
- sequelbox/Tachibana
- sequelbox/Supernova
model-index:
- name: ValiantLabs/Llama3.2-3B-Esper2
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-Shot)
type: Winogrande
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.27
name: acc
- task:
type: text-generation
name: Text Generation
dataset:
name: ARC Challenge (25-Shot)
type: arc-challenge
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 43.17
name: normalized accuracy
model_type: llama
license: llama3.2
QuantFactory/Llama3.2-3B-Esper2-GGUF
This is quantized version of ValiantLabs/Llama3.2-3B-Esper2 created using llama.cpp
Original Model Card
Esper 2 is a DevOps and cloud architecture code specialist built on Llama 3.2 3b.
- Expertise-driven, an AI assistant focused on AWS, Azure, GCP, Terraform, Dockerfiles, pipelines, shell scripts and more!
- Real world problem solving and high quality code instruct performance within the Llama 3.2 Instruct chat format
- Finetuned on synthetic DevOps-instruct and code-instruct data generated with Llama 3.1 405b.
- Overall chat performance supplemented with generalist chat data.
Try our code-instruct AI assistant Enigma!
Version
This is the 2024-10-03 release of Esper 2 for Llama 3.2 3b.
Esper 2 is also available for Llama 3.1 8b!
Esper 2 will be coming to more model sizes soon :)
Prompting Guide
Esper 2 uses the Llama 3.2 Instruct prompt format. The example script below can be used as a starting point for general chat:
import transformers
import torch
model_id = "ValiantLabs/Llama3.2-3B-Esper2"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are an AI assistant."},
{"role": "user", "content": "Hi, how do I optimize the size of a Docker image?"}
]
outputs = pipeline(
messages,
max_new_tokens=2048,
)
print(outputs[0]["generated_text"][-1])
The Model
Esper 2 is built on top of Llama 3.2 3b Instruct, improving performance through high quality DevOps, code, and chat data in Llama 3.2 Instruct prompt style.
Our current version of Esper 2 is trained on DevOps data from sequelbox/Titanium, supplemented by code-instruct data from sequelbox/Tachibana and general chat data from sequelbox/Supernova.
Esper 2 is created by Valiant Labs.
Follow us on X for updates on our models!
We care about open source. For everyone to use.
We encourage others to finetune further from our models.