Instructions to use nbeerbower/flammen-GGUF-Q4_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nbeerbower/flammen-GGUF-Q4_K_M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="nbeerbower/flammen-GGUF-Q4_K_M")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nbeerbower/flammen-GGUF-Q4_K_M")
model = AutoModelForCausalLM.from_pretrained("nbeerbower/flammen-GGUF-Q4_K_M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use nbeerbower/flammen-GGUF-Q4_K_M 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 nbeerbower/flammen-GGUF-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nbeerbower/flammen-GGUF-Q4_K_M:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nbeerbower/flammen-GGUF-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nbeerbower/flammen-GGUF-Q4_K_M: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 nbeerbower/flammen-GGUF-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf nbeerbower/flammen-GGUF-Q4_K_M: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 nbeerbower/flammen-GGUF-Q4_K_M:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nbeerbower/flammen-GGUF-Q4_K_M:Q4_K_M

Use Docker

docker model run hf.co/nbeerbower/flammen-GGUF-Q4_K_M:Q4_K_M

LM Studio
Jan

vLLM

How to use nbeerbower/flammen-GGUF-Q4_K_M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nbeerbower/flammen-GGUF-Q4_K_M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nbeerbower/flammen-GGUF-Q4_K_M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nbeerbower/flammen-GGUF-Q4_K_M:Q4_K_M

SGLang

How to use nbeerbower/flammen-GGUF-Q4_K_M with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "nbeerbower/flammen-GGUF-Q4_K_M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nbeerbower/flammen-GGUF-Q4_K_M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "nbeerbower/flammen-GGUF-Q4_K_M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nbeerbower/flammen-GGUF-Q4_K_M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use nbeerbower/flammen-GGUF-Q4_K_M with Ollama:
```
ollama run hf.co/nbeerbower/flammen-GGUF-Q4_K_M:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use nbeerbower/flammen-GGUF-Q4_K_M with Docker Model Runner:
```
docker model run hf.co/nbeerbower/flammen-GGUF-Q4_K_M:Q4_K_M
```

Lemonade

How to use nbeerbower/flammen-GGUF-Q4_K_M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nbeerbower/flammen-GGUF-Q4_K_M:Q4_K_M

Run and chat with the model

lemonade run user.flammen-GGUF-Q4_K_M-Q4_K_M

List all available models

lemonade list

Atomic Chat

flammen

This is a merge of pre-trained language models created using mergekit.

Quantized using llama.cpp.

Merge Details

Merge Method

This model was merged using the TIES merge method using bardsai/jaskier-7b-dpo-v5.6 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: bardsai/jaskier-7b-dpo-v5.6
  - model: nbeerbower/bruphin-zeta
    parameters:
      density: 0.5
      weight: 0.5
  - model: Gille/StrangeMerges_16-7B-slerp
    parameters:
      density: 0.5
      weight: 0.3
merge_method: ties
base_model: bardsai/jaskier-7b-dpo-v5.6
parameters:
  normalize: true
dtype: bfloat16