Instructions to use kromvault/L3.1-Ablaze-Vulca-v0.1-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kromvault/L3.1-Ablaze-Vulca-v0.1-8B with Transformers:

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

pipe = pipeline("text-generation", model="kromvault/L3.1-Ablaze-Vulca-v0.1-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kromvault/L3.1-Ablaze-Vulca-v0.1-8B")
model = AutoModelForCausalLM.from_pretrained("kromvault/L3.1-Ablaze-Vulca-v0.1-8B", 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 kromvault/L3.1-Ablaze-Vulca-v0.1-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kromvault/L3.1-Ablaze-Vulca-v0.1-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kromvault/L3.1-Ablaze-Vulca-v0.1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/kromvault/L3.1-Ablaze-Vulca-v0.1-8B

SGLang

How to use kromvault/L3.1-Ablaze-Vulca-v0.1-8B 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 "kromvault/L3.1-Ablaze-Vulca-v0.1-8B" \
    --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": "kromvault/L3.1-Ablaze-Vulca-v0.1-8B",
		"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 "kromvault/L3.1-Ablaze-Vulca-v0.1-8B" \
        --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": "kromvault/L3.1-Ablaze-Vulca-v0.1-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use kromvault/L3.1-Ablaze-Vulca-v0.1-8B with Docker Model Runner:
```
docker model run hf.co/kromvault/L3.1-Ablaze-Vulca-v0.1-8B
```

awesome model

by deleted - opened May 10, 2025

Discussion

deleted

May 10, 2025

For some reason I prefer this to the newer Aglow Vulca release, it's nicer for more short-form conversation with the proper prompt. Both models are still great though. Great work.

kromeurus

Kromeurus Vault org May 10, 2025

Oh shit, didn't realize the archive models were still being used lmao. I've seen preference sway between the two but both models have their use cases. Thanks for trying them out tho. Glad you like them.

kromeurus changed discussion status to closed May 10, 2025

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