Instructions to use djuna/Q2.5-Veltha-14B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use djuna/Q2.5-Veltha-14B with Transformers:

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

pipe = pipeline("text-generation", model="djuna/Q2.5-Veltha-14B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("djuna/Q2.5-Veltha-14B")
model = AutoModelForMultimodalLM.from_pretrained("djuna/Q2.5-Veltha-14B")
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 djuna/Q2.5-Veltha-14B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "djuna/Q2.5-Veltha-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": "djuna/Q2.5-Veltha-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/djuna/Q2.5-Veltha-14B

SGLang

How to use djuna/Q2.5-Veltha-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 "djuna/Q2.5-Veltha-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": "djuna/Q2.5-Veltha-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 "djuna/Q2.5-Veltha-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": "djuna/Q2.5-Veltha-14B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use djuna/Q2.5-Veltha-14B with Docker Model Runner:
```
docker model run hf.co/djuna/Q2.5-Veltha-14B
```

Q2.5-Veltha-14B / README.md

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	---
	library_name: transformers
	tags:
	- mergekit
	- merge
	base_model:
	- huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2
	- EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2
	- v000000/Qwen2.5-Lumen-14B
	- qwen/Qwen2.5-14b
	- arcee-ai/SuperNova-Medius
	- allura-org/TQ2.5-14B-Aletheia-v1
	model-index:
	- name: Q2.5-Veltha-14B
	results:
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: IFEval (0-Shot)
	type: HuggingFaceH4/ifeval
	args:
	num_few_shot: 0
	metrics:
	- type: inst_level_strict_acc and prompt_level_strict_acc
	value: 82.92
	name: strict accuracy
	source:
	url: >-
	https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/Q2.5-Veltha-14B
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: BBH (3-Shot)
	type: BBH
	args:
	num_few_shot: 3
	metrics:
	- type: acc_norm
	value: 49.75
	name: normalized accuracy
	source:
	url: >-
	https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/Q2.5-Veltha-14B
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MATH Lvl 5 (4-Shot)
	type: hendrycks/competition_math
	args:
	num_few_shot: 4
	metrics:
	- type: exact_match
	value: 28.02
	name: exact match
	source:
	url: >-
	https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/Q2.5-Veltha-14B
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: GPQA (0-shot)
	type: Idavidrein/gpqa
	args:
	num_few_shot: 0
	metrics:
	- type: acc_norm
	value: 14.54
	name: acc_norm
	source:
	url: >-
	https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/Q2.5-Veltha-14B
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MuSR (0-shot)
	type: TAUR-Lab/MuSR
	args:
	num_few_shot: 0
	metrics:
	- type: acc_norm
	value: 12.26
	name: acc_norm
	source:
	url: >-
	https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/Q2.5-Veltha-14B
	name: Open LLM Leaderboard
	- task:
	type: text-generation
	name: Text Generation
	dataset:
	name: MMLU-PRO (5-shot)
	type: TIGER-Lab/MMLU-Pro
	config: main
	split: test
	args:
	num_few_shot: 5
	metrics:
	- type: acc
	value: 47.76
	name: accuracy
	source:
	url: >-
	https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=djuna/Q2.5-Veltha-14B
	name: Open LLM Leaderboard
	new_version: djuna/Q2.5-Veltha-14B-0.5
	---
	# merge

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the della_linear merge method using [qwen/Qwen2.5-14b](https://huggingface.co/qwen/Qwen2.5-14b) as a base.

	### Models Merged

	The following models were included in the merge:
	* [huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2](https://huggingface.co/huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2)
	* [EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2](https://huggingface.co/EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2)
	* [v000000/Qwen2.5-Lumen-14B](https://huggingface.co/v000000/Qwen2.5-Lumen-14B)
	* [arcee-ai/SuperNova-Medius](https://huggingface.co/arcee-ai/SuperNova-Medius)
	* [allura-org/TQ2.5-14B-Aletheia-v1](https://huggingface.co/allura-org/TQ2.5-14B-Aletheia-v1)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	merge_method: della_linear
	dtype: float32
	out_dtype: bfloat16
	parameters:
	epsilon: 0.04
	lambda: 1.05
	normalize: true
	base_model: qwen/Qwen2.5-14b
	tokenizer_source: arcee-ai/SuperNova-Medius
	models:
	- model: arcee-ai/SuperNova-Medius
	parameters:
	weight: 10
	density: 1
	- model: EVA-UNIT-01/EVA-Qwen2.5-14B-v0.2
	parameters:
	weight: 7
	density: 0.5
	- model: v000000/Qwen2.5-Lumen-14B
	parameters:
	weight: 7
	density: 0.4
	- model: allura-org/TQ2.5-14B-Aletheia-v1
	parameters:
	weight: 8
	density: 0.4
	- model: huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2
	parameters:
	weight: 8
	density: 0.45
	```

	# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
	Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/djuna__Q2.5-Veltha-14B-details)

	\| Metric \|Value\|
	\|-------------------\|----:\|
	\|Avg. \|39.21\|
	\|IFEval (0-Shot) \|82.92\|
	\|BBH (3-Shot) \|49.75\|
	\|MATH Lvl 5 (4-Shot)\|28.02\|
	\|GPQA (0-shot) \|14.54\|
	\|MuSR (0-shot) \|12.26\|
	\|MMLU-PRO (5-shot) \|47.76\|