Instructions to use Azazelle/L3-Hecate-8B-v1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Azazelle/L3-Hecate-8B-v1.2 with Transformers:

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

pipe = pipeline("text-generation", model="Azazelle/L3-Hecate-8B-v1.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Azazelle/L3-Hecate-8B-v1.2")
model = AutoModelForCausalLM.from_pretrained("Azazelle/L3-Hecate-8B-v1.2", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Azazelle/L3-Hecate-8B-v1.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Azazelle/L3-Hecate-8B-v1.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Azazelle/L3-Hecate-8B-v1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Azazelle/L3-Hecate-8B-v1.2

SGLang

How to use Azazelle/L3-Hecate-8B-v1.2 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 "Azazelle/L3-Hecate-8B-v1.2" \
    --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": "Azazelle/L3-Hecate-8B-v1.2",
		"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 "Azazelle/L3-Hecate-8B-v1.2" \
        --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": "Azazelle/L3-Hecate-8B-v1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Azazelle/L3-Hecate-8B-v1.2 with Docker Model Runner:
```
docker model run hf.co/Azazelle/L3-Hecate-8B-v1.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

L3-Hecate-8B-v1.2

About:

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

Recommended Samplers:

Temperature - 1.0
TFS - 0.7
Smoothing Factor - 0.3
Smoothing Curve - 1.1
Repetition Penalty - 1.08

Merge Method

This model was merged a series of model stock, followed by ExPO. It uses a mix of roleplay models to improve performance.

Configuration

The following YAML configuration was used to produce this model:

---
# Concise-Mopey
models:
  - model: Salesforce/LLaMA-3-8B-SFR-Iterative-DPO-Concise-R
    parameters:
      weight: 1.0
  - model: failspy/Llama-3-8B-Instruct-MopeyMule
    parameters:
      weight: 1.0
merge_method: task_arithmetic
base_model: NousResearch/Meta-Llama-3-8B-Instruct
parameters:
  normalize: false
dtype: float32
vocab_type: bpe
name: Concise-Mopey

---
# Mopey RP Mix
models:
  - model: Concise-Mopey+Azazelle/Llama-3-Sunfall-8b-lora
  - model: Concise-Mopey+Azazelle/Llama-3-8B-Abomination-LORA
  - model: Concise-Mopey+Azazelle/llama3-8b-hikikomori-v0.4
  - model: Concise-Mopey+Azazelle/Llama-3-Instruct-LiPPA-LoRA-8B
  - model: Concise-Mopey+Azazelle/BlueMoon_Llama3
  - model: Concise-Mopey+Azazelle/Llama3_RP_ORPO_LoRA
  - model: Concise-Mopey+mpasila/Llama-3-LimaRP-Instruct-LoRA-8B
  - model: Concise-Mopey+Azazelle/Llama-3-LongStory-LORA
merge_method: model_stock
base_model: failspy/Llama-3-8B-Instruct-MopeyMule
dtype: float32
vocab_type: bpe
name: mopey_rp

---
models:
  - model: Nitral-AI/Hathor_Tahsin-L3-8B-v0.85
  - model: Sao10K/L3-8B-Tamamo-v1
  - model: Sao10K/L3-8B-Niitama-v1
  - model: Hastagaras/Jamet-8B-L3-MK.V-Blackroot
  - model: nothingiisreal/L3-8B-Celeste-v1
  - model: Jellywibble/lora_120k_pref_data_ep2
  - model: Nitral-AI/Hathor_Stable-v0.2-L3-8B
  - model: mopey_rp
merge_method: model_stock
base_model: NousResearch/Meta-Llama-3-8B-Instruct
dtype: float32
vocab_type: bpe
name: hq_rp

---
# ExPO
models:
  - model: hq_rp
    parameters:
      weight:
        - filter: mlp
          value: 1.15
        - filter: self_attn
          value: 1.025
        - value: 1.0
merge_method: task_arithmetic
base_model: NousResearch/Meta-Llama-3-8B-Instruct
parameters:
  normalize: false
dtype: float32
vocab_type: bpe

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Safetensors

Model size

8B params

Tensor type

F32

Model tree for Azazelle/L3-Hecate-8B-v1.2

Base model

NousResearch/Meta-Llama-3-8B-Instruct

Finetuned

(21)

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Quantizations

2 models