Instructions to use rootxhacker/Apollo-24B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rootxhacker/Apollo-24B with Transformers:

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

pipe = pipeline("text-generation", model="rootxhacker/Apollo-24B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("rootxhacker/Apollo-24B")
model = AutoModelForCausalLM.from_pretrained("rootxhacker/Apollo-24B")
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

vLLM

How to use rootxhacker/Apollo-24B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rootxhacker/Apollo-24B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rootxhacker/Apollo-24B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rootxhacker/Apollo-24B

SGLang

How to use rootxhacker/Apollo-24B 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 "rootxhacker/Apollo-24B" \
    --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": "rootxhacker/Apollo-24B",
		"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 "rootxhacker/Apollo-24B" \
        --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": "rootxhacker/Apollo-24B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use rootxhacker/Apollo-24B with Docker Model Runner:
```
docker model run hf.co/rootxhacker/Apollo-24B
```

Apollo-24B / README.md

rootxhacker

Update README.md

a33a3fb verified about 1 year ago

preview code

raw

history blame contribute delete

2.17 kB

	---
	base_model:
	- allura-org/Mistral-Small-Sisyphus-24b-2503
	- yentinglin/Mistral-Small-24B-Instruct-2501-reasoning
	- arcee-ai/Arcee-Blitz
	- huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
	- cognitivecomputations/Dolphin3.0-R1-Mistral-24B
	- cognitivecomputations/Dolphin3.0-Mistral-24B
	- ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4
	library_name: transformers
	tags:
	- mergekit
	- merge
	license: mit
	---
	# 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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [cognitivecomputations/Dolphin3.0-Mistral-24B](https://huggingface.co/cognitivecomputations/Dolphin3.0-Mistral-24B) as a base.

	### Models Merged

	The following models were included in the merge:
	* [allura-org/Mistral-Small-Sisyphus-24b-2503](https://huggingface.co/allura-org/Mistral-Small-Sisyphus-24b-2503)
	* [yentinglin/Mistral-Small-24B-Instruct-2501-reasoning](https://huggingface.co/yentinglin/Mistral-Small-24B-Instruct-2501-reasoning)
	* [arcee-ai/Arcee-Blitz](https://huggingface.co/arcee-ai/Arcee-Blitz)
	* [huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated](https://huggingface.co/huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated)
	* [cognitivecomputations/Dolphin3.0-R1-Mistral-24B](https://huggingface.co/cognitivecomputations/Dolphin3.0-R1-Mistral-24B)
	* [ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4](https://huggingface.co/ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	models:
	- model: arcee-ai/Arcee-Blitz
	- model: cognitivecomputations/Dolphin3.0-R1-Mistral-24B
	- model: cognitivecomputations/Dolphin3.0-Mistral-24B
	- model: yentinglin/Mistral-Small-24B-Instruct-2501-reasoning
	- model: ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4
	- model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
	- model: allura-org/Mistral-Small-Sisyphus-24b-2503

	merge_method: model_stock
	base_model: cognitivecomputations/Dolphin3.0-Mistral-24B
	normalize: true
	int8_mask: true
	dtype: bfloat16

	```