Instructions to use sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic with Transformers:

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

pipe = pipeline("text-generation", model="sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic")
model = AutoModelForCausalLM.from_pretrained("sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic")
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 sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic

SGLang

How to use sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic 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 "sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic" \
    --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": "sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic",
		"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 "sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic" \
        --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": "sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic with Docker Model Runner:
```
docker model run hf.co/sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is a decensored version of SteelStorage/L3.1-MS-Astoria-70b-v2, made using Heretic v1.1.0

Abliteration parameters

Parameter	Value
direction_index	67.05
attn.o_proj.max_weight	1.40
attn.o_proj.max_weight_position	47.63
attn.o_proj.min_weight	1.04
attn.o_proj.min_weight_distance	13.15
mlp.down_proj.max_weight	1.40
mlp.down_proj.max_weight_position	66.24
mlp.down_proj.min_weight	1.24
mlp.down_proj.min_weight_distance	43.14

Performance

Metric	This model	Original model (SteelStorage/L3.1-MS-Astoria-70b-v2)
KL divergence	0.0268	0 (by definition)
Refusals	11/100	93/100

L3.1-MS-Astoria-70b-v2 Data Card

Now the cute anime girl has your attention

Creator: SteelSkull

About Astoria-70b-v2:

Name Legend:
L3.1 = Llama 3.1
MS = Model Stock
70B = its 70B

This model is a remake of the original astoria with modern models and context sizes its goal is to merge the robust storytelling of mutiple models while attempting to maintain intelligence.

Use Llama 3 Format or meth format (llama 3 refuses to work with stepped thinking but meth works)

Quants: (List of badasses)

GGUF Quant:

- bartowski: Combined-GGUF

- mradermacher: GGUF // Imat-GGUF

Config:

MODEL_NAME = "L3.1-MS-Astoria-70b-v2"
base_model: mlabonne/Llama-3.1-70B-Instruct-lorablated
merge_method: model_stock
dtype: bfloat16
models:
  - model: migtissera/Tess-3-Llama-3.1-70B
  - model: NeverSleep/Lumimaid-v0.2-70B
  - model: Sao10K/L3.1-70B-Euryale-v2.2
  - model: ArliAI/Llama-3.1-70B-ArliAI-RPMax-v1.2
  - model: nbeerbower/Llama3.1-Gutenberg-Doppel-70B

If you wish to support:

Downloads last month: 1

Safetensors

Model size

71B params

Tensor type

BF16

Model tree for sh0ck0r/L3.1-MS-Astoria-70b-v2-heretic

ArliAI/Llama-3.1-70B-ArliAI-RPMax-v1.2

NeverSleep/Lumimaid-v0.2-70B

Sao10K/L3.1-70B-Euryale-v2.2

migtissera/Tess-3-Llama-3.1-70B

mlabonne/Llama-3.1-70B-Instruct-lorablated

nbeerbower/Llama3.1-Gutenberg-Doppel-70B

Merge model

this model

Quantizations

2 models