Instructions to use RichardErkhov/besimray_-_test-model-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 RichardErkhov/besimray_-_test-model-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 RichardErkhov/besimray_-_test-model-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/besimray_-_test-model-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 RichardErkhov/besimray_-_test-model-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf RichardErkhov/besimray_-_test-model-gguf:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf RichardErkhov/besimray_-_test-model-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf RichardErkhov/besimray_-_test-model-gguf:Q4_K_M

Use Docker

docker model run hf.co/RichardErkhov/besimray_-_test-model-gguf:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use RichardErkhov/besimray_-_test-model-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/besimray_-_test-model-gguf:Q4_K_M
```
Unsloth Desktop

How to use RichardErkhov/besimray_-_test-model-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RichardErkhov/besimray_-_test-model-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": "RichardErkhov/besimray_-_test-model-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use RichardErkhov/besimray_-_test-model-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/besimray_-_test-model-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/besimray_-_test-model-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/besimray_-_test-model-gguf:Q4_K_M

Run and chat with the model

lemonade run user.besimray_-_test-model-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use RichardErkhov/besimray_-_test-model-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 RichardErkhov/besimray_-_test-model-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 RichardErkhov/besimray_-_test-model-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use RichardErkhov/besimray_-_test-model-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf RichardErkhov/besimray_-_test-model-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 "RichardErkhov/besimray_-_test-model-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"

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

test-model - GGUF

Model creator: https://huggingface.co/besimray/
Original model: https://huggingface.co/besimray/test-model/

Name	Quant method	Size
test-model.Q2_K.gguf	Q2_K	1.27GB
test-model.IQ3_XS.gguf	IQ3_XS	1.38GB
test-model.IQ3_S.gguf	IQ3_S	1.44GB
test-model.Q3_K_S.gguf	Q3_K_S	1.44GB
test-model.IQ3_M.gguf	IQ3_M	1.49GB
test-model.Q3_K.gguf	Q3_K	1.57GB
test-model.Q3_K_M.gguf	Q3_K_M	1.57GB
test-model.Q3_K_L.gguf	Q3_K_L	1.69GB
test-model.IQ4_XS.gguf	IQ4_XS	1.71GB
test-model.Q4_0.gguf	Q4_0	1.79GB
test-model.IQ4_NL.gguf	IQ4_NL	1.79GB
test-model.Q4_K_S.gguf	Q4_K_S	1.8GB
test-model.Q4_K.gguf	Q4_K	1.88GB
test-model.Q4_K_M.gguf	Q4_K_M	1.88GB
test-model.Q4_1.gguf	Q4_1	1.95GB
test-model.Q5_0.gguf	Q5_0	2.11GB
test-model.Q5_K_S.gguf	Q5_K_S	2.11GB
test-model.Q5_K.gguf	Q5_K	2.16GB
test-model.Q5_K_M.gguf	Q5_K_M	2.16GB
test-model.Q5_1.gguf	Q5_1	2.28GB
test-model.Q6_K.gguf	Q6_K	2.46GB
test-model.Q8_0.gguf	Q8_0	3.19GB

Original model description:

base_model: meta-llama/Llama-3.2-3B-Instruct language: - en library_name: transformers license: llama3.2 tags: - llama-3 - llama - meta - facebook - unsloth - transformers

Finetune Llama 3.2, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!

We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing

unsloth/Llama-3.2-3B-Instruct

For more details on the model, please go to Meta's original model card

✨ Finetune for Free

All notebooks are beginner friendly! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.

Unsloth supports	Free Notebooks	Performance	Memory use
Llama-3.2 (3B)	▶️ Start on Colab	2.4x faster	58% less
Llama-3.1 (11B vision)	▶️ Start on Colab	2.4x faster	58% less
Llama-3.1 (8B)	▶️ Start on Colab	2.4x faster	58% less
Phi-3.5 (mini)	▶️ Start on Colab	2x faster	50% less
Gemma 2 (9B)	▶️ Start on Colab	2.4x faster	58% less
Mistral (7B)	▶️ Start on Colab	2.2x faster	62% less
DPO - Zephyr	▶️ Start on Colab	1.9x faster	19% less

This conversational notebook is useful for ShareGPT ChatML / Vicuna templates.
This text completion notebook is for raw text. This DPO notebook replicates Zephyr.
* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.

Special Thanks

A huge thank you to the Meta and Llama team for creating and releasing these models.

Model Information

The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.

Model developer: Meta

Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.

Llama 3.2 family of models Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.

Model Release Date: Sept 25, 2024

Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.

License: Use of Llama 3.2 is governed by the Llama 3.2 Community License (a custom, commercial license agreement).

Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model README. For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go here.

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