Instructions to use IHaBiS/TurdusDareBeagle-7B-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IHaBiS/TurdusDareBeagle-7B-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="IHaBiS/TurdusDareBeagle-7B-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IHaBiS/TurdusDareBeagle-7B-exl2")
model = AutoModelForCausalLM.from_pretrained("IHaBiS/TurdusDareBeagle-7B-exl2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use IHaBiS/TurdusDareBeagle-7B-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IHaBiS/TurdusDareBeagle-7B-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IHaBiS/TurdusDareBeagle-7B-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/IHaBiS/TurdusDareBeagle-7B-exl2

SGLang

How to use IHaBiS/TurdusDareBeagle-7B-exl2 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 "IHaBiS/TurdusDareBeagle-7B-exl2" \
    --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": "IHaBiS/TurdusDareBeagle-7B-exl2",
		"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 "IHaBiS/TurdusDareBeagle-7B-exl2" \
        --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": "IHaBiS/TurdusDareBeagle-7B-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use IHaBiS/TurdusDareBeagle-7B-exl2 with Docker Model Runner:
```
docker model run hf.co/IHaBiS/TurdusDareBeagle-7B-exl2
```

Exl2 version of Undi95/OpenDolphinMaid-4x7b

branch

main : 8bpw h8
b8h8 : 8bpw h8

Using ThePile 0007.parquet as dataset

Quantization settings : python convert.py -i models/flemmingmiguel_TurdusDareBeagle-7B -o TurdusDareBeagle-7B-temp -cf TurdusDareBeagle-7B-8bpw-h8-exl2 -c 0007.parquet -l 8192 -b 8 -hb 8 -ml 8192

below this line is original readme

TurdusDareBeagle-7B

TurdusDareBeagle-7B is a merge of the following models using LazyMergekit:

As an experiment to find the best base merge to further fine-tuning, expect a lot of experiments named using parts of the component models until a clear winner emerges in the benchmarks

In this case .

🧩 Configuration

slices:
    - sources:
      - model: udkai/Turdus
        layer_range: [0, 32]
      - model: flemmingmiguel/DareBeagle-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: flemmingmiguel/DareBeagle-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.45 # fallback for rest of tensors
dtype: float16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "flemmingmiguel/TurdusDareBeagle-7B"
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"])

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