QuantFactory/Llama3.1-8B-Esper2-GGUF

language:

• —en pipeline_tag: text-generation tags:
• —esper
• —esper-2
• —valiant
• —valiant-labs
• —llama
• —llama-3.1
• —llama-3.1-instruct
• —llama-3.1-instruct-8b
• —llama-3
• —llama-3-instruct
• —llama-3-instruct-8b
• —8b
• —code
• —code-instruct
• —python
• —dev-ops
• —terraform
• —azure
• —aws
• —gcp
• —architect
• —engineer
• —developer
• —conversational
• —chat
• —instruct base_model: meta-llama/Meta-Llama-3.1-8B-Instruct datasets:
• —sequelbox/Titanium
• —sequelbox/Tachibana
• —sequelbox/Supernova model_type: llama model-index:
• —name: ValiantLabs/Llama3.1-8B-Esper2 results:
• —task: type: text-generation name: Text Generation dataset: name: Winogrande (5-Shot) type: Winogrande args: numfewshot: 5 metrics:
• —type: acc value: 75.85 name: acc license: llama3.1


QuantFactory/Llama3.1-8B-Esper2-GGUF

This is quantized version of ValiantLabs/Llama3.1-8B-Esper2 created using llama.cpp

Original Model Card

Esper 2 is a DevOps and cloud architecture code specialist built on Llama 3.1 8b.

• —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 3.1 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 2024-10-02 release of Esper 2 for Llama 3.1 8b.

Esper 2 is now available for Llama 3.2 3b!

Esper 2 will be coming to more model sizes soon :)

Prompting Guide

Esper 2 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-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 3.1 8b Instruct, improving performance through high quality DevOps, code, and chat data in Llama 3.1 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.

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

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.