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
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

More experiments that actually work LMAO. Started straying away from Siithamo, at least model list wise. Stheno is just so chatty, idk how to tame it yet. Used components from my upcoming fatboy model as parts of this merge and imo, this is a hidden gem.

09.27.2024: Well this model needed way more help then i thought it did. So a month later, I come with a new iteration Aglow Vulca. Use that one, since this has bugs.

Quants

OG Q8 GGUF by me.

Model Details & Recommended Settings

(Still testing; details subject to change)

Follows instructs fairly well, doesn't stray much unless the temp is too high. Same thing as all the other model I make with Formax (ty ArliAI), this merge will reflect the character card quality; shit card will have shit output and vise versa.

Generates slightly flowery text, thought process type writing. Human-ish dialogue. Chatty but not too chatty, will mimic previous text examples. Coherent up to 16k (as tested).

Rec. Settings:

Template: L3
Temperature: 1.3
Min P: 0.1
Repeat Penalty: 1.05
Repeat Penalty Tokens: 256-512 #stick closer to 256

Merge Theory

Will update later, too tired rn.

Config

models: 
  - model: ArliAI/ArliAI-Llama-3-8B-Formax-v1.0
    parameters:
      weight: [1, 1, 1, 1, 0, 0, 0, 0]
  - model: gradientai/Llama-3-8B-Instruct-Gradient-1048k
    parameters:
      weight: [0, 0, 0, 0, 1, 1, 1, 1]
base_model: ArliAI/Llama-3.1-8B-ArliAI-Formax-v1.0
parameters:
  normalize: false
  int8_mask: true
merge_method: dare_linear
dtype: float32
out_dtype: bfloat16
tokenizer_source: base
name: formaxext.3.1
---
models:
    - model: kromcomp/L3-Ceto-Epith-Humanity.A-v0.1-8B
    - model: ghost-x/ghost-8b-beta-1608
    - model: kromcomp/L3-Ceto-Epith-Humanity-v0.1-8B
base_model: tohur/natsumura-storytelling-rp-1.0-llama-3.1-8b
parameters:
  normalize: false
  int8_mask: true
merge_method: model_stock
dtype: float32
out_dtype: bfloat16
tokenizer_source: base
name: humplus
---
models: 
  - model: humplus
    parameters:
      weight: [0.01, 0.53, 0.9]
  - model: ArliAI/Llama-3.1-8B-ArliAI-RPMax-v1.1
    parameters:
      weight: [0.55, 0.29, 0.1]
  - model: crestf411/L3.1-8B-sunfall-v0.6.1-dpo
    parameters:
      weight: [0.54, 0.28, 0.1]
base_model: humplus
parameters:
  normalize: false
  int8_mask: true
merge_method: dare_linear
dtype: float32
out_dtype: bfloat16
tokenizer_source: base
name: tusl3.1
---
models: 
  - model: tusl3.1
    parameters:
      weight: [0.5, 0.75, 0.8, 0.9, 0.95]
      density: 0.9
      gamma: 0.01
  - model: formaxext.3.1
    parameters:
      weight: [0.5, 0.25, 0.2, 0.1, 0.05]
      density: 0.9
      gamma: 0.01
base_model: tusl3.1
tokenizer_source: union
parameters:
  normalize: false
  int8_mask: true
merge_method: breadcrumbs_ties
dtype: float32
out_dtype: bfloat16
name: mantusl3.1