Instructions to use v000000/Qwen2.5-Lumen-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use v000000/Qwen2.5-Lumen-14B with Transformers:

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

pipe = pipeline("text-generation", model="v000000/Qwen2.5-Lumen-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("v000000/Qwen2.5-Lumen-14B")
model = AutoModelForCausalLM.from_pretrained("v000000/Qwen2.5-Lumen-14B", device_map="auto")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use v000000/Qwen2.5-Lumen-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "v000000/Qwen2.5-Lumen-14B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "v000000/Qwen2.5-Lumen-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/v000000/Qwen2.5-Lumen-14B

SGLang

How to use v000000/Qwen2.5-Lumen-14B 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 "v000000/Qwen2.5-Lumen-14B" \
    --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": "v000000/Qwen2.5-Lumen-14B",
		"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 "v000000/Qwen2.5-Lumen-14B" \
        --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": "v000000/Qwen2.5-Lumen-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use v000000/Qwen2.5-Lumen-14B with Docker Model Runner:
```
docker model run hf.co/v000000/Qwen2.5-Lumen-14B
```

Qwen2.5-Lumen-14B / README.md

v000000

Upload folder using huggingface_hub

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base_model:
  - v000000/Qwen2.5-14B-Gutenberg-1e-Delta
  - v000000/qwen_results37
  - v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno
  - v000000/Qwen2.5-14B-Gutenberg-UltraLambda-Slerpeno
  - v000000/Qwen2.5-14B-Gutenberg-0.25e-Early-STACKED-0.37e
  - Qwen/Qwen2.5-14B-Instruct
  - v000000/qwen_results100
  - v000000/Qwen2.5-14B-Gutenberg-1e-Delta
  - v000000/Qwen2.5-14B-Gutenberg-0.25e-Early
  - tanliboy/lambda-qwen2.5-14b-dpo-test
  - Qwen/Qwen2.5-14B-Instruct
  - v000000/qwen_results37
  - v000000/Qwen2.5-14B-Gutenberg-0.25e-Early-STACKED-0.37e
  - v000000/qwen_results100
library_name: transformers
tags:
  - mergekit
  - merge

merge

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

Merge Details

Merge Method

This model was merged using the Model Stock merge method using v000000/Qwen2.5-14B-Gutenberg-1e-Delta 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: v000000/Qwen2.5-14B-Gutenberg-1e-Delta
  - model: v000000/Qwen2.5-14B-Gutenberg-0.25e-Early-STACKED-0.37e #(Qwen2.5-14B-Gutenberg-0.6e-Sequential)
  - model: v000000/Qwen2.5-14B-Gutenberg-0.25e-Early
  - model: v000000/Qwen2.5-14B-Gutenberg-1e-Delta+v000000/qwen_results37 #(Qwen2.5-14B-Gutenberg-2e-Sequential)
  - model: Qwen/Qwen2.5-14B-Instruct+v000000/qwen_results37 #(Qwen2.5-14B-Gutenberg-0.37e-Early)
  - model: v000000/Qwen2.5-14B-Gutenberg-0.25e-Early-STACKED-0.37e+v000000/qwen_results100 #(Qwen2.5-14B-Gutenberg-1e-Zeta)
  - model: Qwen/Qwen2.5-14B-Instruct+v000000/qwen_results100 #(Qwen2.5-14B-Gutenberg-1e-Theta)
  - model: tanliboy/lambda-qwen2.5-14b-dpo-test
  - model: v000000/Qwen2.5-14B-Gutenberg-1e-Delta
  - model: tanliboy/lambda-qwen2.5-14b-dpo-test
  - model: v000000/Qwen2.5-14B-Gutenberg-UltraLambda-Slerpeno
  - model: v000000/Qwen2.5-14B-Gutenberg-Instruct-Slerpeno
base_model: v000000/Qwen2.5-14B-Gutenberg-1e-Delta
merge_method: model_stock
dtype: bfloat16