Instructions to use QuantFactory/ChimeraLlama-3-8B-v3-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/ChimeraLlama-3-8B-v3-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/ChimeraLlama-3-8B-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/ChimeraLlama-3-8B-v3-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/ChimeraLlama-3-8B-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/ChimeraLlama-3-8B-v3-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/ChimeraLlama-3-8B-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/ChimeraLlama-3-8B-v3-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/ChimeraLlama-3-8B-v3-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/ChimeraLlama-3-8B-v3-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/ChimeraLlama-3-8B-v3-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/ChimeraLlama-3-8B-v3-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/ChimeraLlama-3-8B-v3-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/ChimeraLlama-3-8B-v3-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/ChimeraLlama-3-8B-v3-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/ChimeraLlama-3-8B-v3-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/ChimeraLlama-3-8B-v3-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/ChimeraLlama-3-8B-v3-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/ChimeraLlama-3-8B-v3-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/ChimeraLlama-3-8B-v3-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/ChimeraLlama-3-8B-v3-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ChimeraLlama-3-8B-v3-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

QuantFactory/ChimeraLlama-3-8B-v3-GGUF

This is quantized version of mlabonne/ChimeraLlama-3-8B-v3 created using llama.cpp

Original Model Card

ChimeraLlama-3-8B-v3

ChimeraLlama-3-8B-v3 is a merge of the following models using LazyMergekit:

🧩 Configuration

models:
  - model: NousResearch/Meta-Llama-3-8B
    # No parameters necessary for base model
  - model: NousResearch/Meta-Llama-3-8B-Instruct
    parameters:
      density: 0.6
      weight: 0.5
  - model: mlabonne/OrpoLlama-3-8B
    parameters:
      density: 0.55
      weight: 0.05
  - model: cognitivecomputations/dolphin-2.9-llama3-8b
    parameters:
      density: 0.55
      weight: 0.05
  - model: Danielbrdz/Barcenas-Llama3-8b-ORPO
    parameters:
      density: 0.55
      weight: 0.2
  - model: VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
    parameters:
      density: 0.55
      weight: 0.1
  - model: vicgalle/Configurable-Llama-3-8B-v0.3
    parameters:
      density: 0.55
      weight: 0.05
  - model: MaziyarPanahi/Llama-3-8B-Instruct-DPO-v0.3
    parameters:
      density: 0.55
      weight: 0.05
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
  int8_mask: true
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "mlabonne/ChimeraLlama-3-8B-v3"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])