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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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 Desktop

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 serve -hf QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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

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

Hermes Agent

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 serve -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

Atomic Chat

OpenClaw

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

Start the llama.cpp server

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

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "QuantFactory/Llama3.2-3B-Esper2-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

aashish1904 commited on Oct 8, 2024

Commit

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+---
+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 Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+# QuantFactory/Llama3.2-3B-Esper2-GGUF
+This is quantized version of [ValiantLabs/Llama3.2-3B-Esper2](https://huggingface.co/ValiantLabs/Llama3.2-3B-Esper2) created using llama.cpp
+# Original Model Card
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64f267a8a4f79a118e0fcc89/4I6oK8DG0so4VD8GroFsd.jpeg)
+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](https://huggingface.co/datasets/sequelbox/Titanium) and [code-instruct](https://huggingface.co/datasets/sequelbox/Tachibana) data generated with Llama 3.1 405b.
+- Overall chat performance supplemented with [generalist chat data.](https://huggingface.co/datasets/sequelbox/Supernova)
+Try our code-instruct AI assistant [Enigma!](https://huggingface.co/ValiantLabs/Llama3.1-8B-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!](https://huggingface.co/ValiantLabs/Llama3.1-8B-Esper2)
+Esper 2 will be coming to more model sizes soon :)
+## Prompting Guide
+Esper 2 uses the [Llama 3.2 Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) prompt format. The example script below can be used as a starting point for general chat:
+```python
+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](https://huggingface.co/datasets/sequelbox/Titanium), supplemented by code-instruct data from [sequelbox/Tachibana](https://huggingface.co/datasets/sequelbox/Tachibana) and general chat data from [sequelbox/Supernova.](https://huggingface.co/datasets/sequelbox/Supernova)
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/63444f2687964b331809eb55/VCJ8Fmefd8cdVhXSSxJiD.jpeg)
+Esper 2 is created by [Valiant Labs.](http://valiantlabs.ca/)
+[Check out our HuggingFace page for Shining Valiant 2, Enigma, and our other Build Tools models for creators!](https://huggingface.co/ValiantLabs)
+[Follow us on X for updates on our models!](https://twitter.com/valiant_labs)
+We care about open source.
+For everyone to use.
+We encourage others to finetune further from our models.