Instructions to use QuantFactory/Llama3.2-3B-Esper2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Llama3.2-3B-Esper2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Llama3.2-3B-Esper2-GGUF",
	filename="Llama3.2-3B-Esper2.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Llama3.2-3B-Esper2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Llama3.2-3B-Esper2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Llama3.2-3B-Esper2-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Llama3.2-3B-Esper2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Llama3.2-3B-Esper2-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Llama3.2-3B-Esper2-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Llama3.2-3B-Esper2-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Llama3.2-3B-Esper2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Llama3.2-3B-Esper2-GGUF to start chatting

Pi new

How to use QuantFactory/Llama3.2-3B-Esper2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/Llama3.2-3B-Esper2-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/Llama3.2-3B-Esper2-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Llama3.2-3B-Esper2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama3.2-3B-Esper2-GGUF-Q4_K_M

List all available models

lemonade list

Llama3.2-3B-Esper2-GGUF / README.md

aashish1904

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metadata

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.

Check out our HuggingFace page for Shining Valiant 2, Enigma, 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.