Instructions to use Lil-R/BLYMM-Qwen-DareTies-V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lil-R/BLYMM-Qwen-DareTies-V1 with Transformers:

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

pipe = pipeline("text-generation", model="Lil-R/BLYMM-Qwen-DareTies-V1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Lil-R/BLYMM-Qwen-DareTies-V1")
model = AutoModelForMultimodalLM.from_pretrained("Lil-R/BLYMM-Qwen-DareTies-V1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Lil-R/BLYMM-Qwen-DareTies-V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lil-R/BLYMM-Qwen-DareTies-V1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lil-R/BLYMM-Qwen-DareTies-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Lil-R/BLYMM-Qwen-DareTies-V1

SGLang

How to use Lil-R/BLYMM-Qwen-DareTies-V1 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 "Lil-R/BLYMM-Qwen-DareTies-V1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lil-R/BLYMM-Qwen-DareTies-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Lil-R/BLYMM-Qwen-DareTies-V1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lil-R/BLYMM-Qwen-DareTies-V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Lil-R/BLYMM-Qwen-DareTies-V1 with Docker Model Runner:
```
docker model run hf.co/Lil-R/BLYMM-Qwen-DareTies-V1
```

BLYMM-Qwen-DareTies-V1

This model has been produced by:

ROBERGE Marial, engineering student at French Engineering School ECE
ESCRIVA Mathis, engineering student at French Engineering School ECE
LALAIN Youri, engineering student at French Engineering School ECE
RAGE LILIAN, engineering student at French Engineering School ECE
HUVELLE Baptiste, engineering student at French Engineering School ECE

Under the supervision of:

Andre-Louis Rochet, Lecturer at ECE & Co-Founder of TW3 Partners
Paul Lemaistre, CTO of TW3 Partners

With the contribution of:

ECE engineering school as sponsor and financial contributor
François STEPHAN as director of ECE
Gérard REUS as acting director of iLAB
Matthieu JOLLARD ECE Alumni
Louis GARCIA ECE Alumni

Supervisory structure

The iLab (intelligence Lab) is a structure created by the ECE and dedicated to artificial intelligence

About ECE

ECE, a multi-program, multi-campus, and multi-sector engineering school specializing in digital engineering, trains engineers and technology experts for the 21st century, capable of meeting the challenges of the dual digital and sustainable development revolutions.

Caractéristiques

Méthode de fusion : Dare_ties
Modèles sources :
- newsbang/Homer-v1.0-Qwen2.5-72B
- Qwen/Qwen2.5-72B-Instruct

Downloads last month: 2

Safetensors

Model size

73B params

Tensor type

F32