Instructions to use KaraKaraWarehouse/L3.1-70b-Inori with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KaraKaraWarehouse/L3.1-70b-Inori with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KaraKaraWarehouse/L3.1-70b-Inori")
model = AutoModelForCausalLM.from_pretrained("KaraKaraWarehouse/L3.1-70b-Inori", 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 KaraKaraWarehouse/L3.1-70b-Inori with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/KaraKaraWarehouse/L3.1-70b-Inori

SGLang

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

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

KaraKaraWitch/L3.1-70b-Inori

Inori is the second 70b for the weekend for me to play around.

Learning from the previous model, I yeeted Hermes into the atmosphere and used Glitz as a base.

Inori takes a different approach by using Model Stock.

Dracarys (I just threw it in, but can be useful for code)
Euryale (You all know it!)
Cathallama (Athene + turboderp_cat)
New Dawn (I heard people like it so I added it in)
Celeste (RP)
Japanese-Instruct (Enhancement of Japanese Language for the weebs out there.)

No Hermes was harmed in the making of this model stock merge.

L3.1-70b-Inori is a merge of the following models using LazyMergekit:

General Thoughts

This model has some weird censorship issues. Soketimes it triggers that it can't generate explicit text while sometimes it doesn't.
For that reason, I don't recommend people to use this model.

Yap / Chat Format

L3 Instruct.

Quants & Hosts

🧩 Configuration


models:
  - model: Fizzarolli/L3.1-70b-glitz-v0.2
  - model: cyberagent/Llama-3.1-70B-Japanese-Instruct-2407
  - model: Sao10K/L3-70B-Euryale-v2.1
  - model: nothingiisreal/L3.1-70B-Celeste-V0.1-BF16
  - model: sophosympatheia/New-Dawn-Llama-3.1-70B-v1.1
  - model: gbueno86/Cathallama-70B
  - model: abacusai/Dracarys-Llama-3.1-70B-Instruct

merge_method: model_stock
base_model: Fizzarolli/L3.1-70b-glitz-v0.2
parameters:
  normalize: true
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "KaraKaraWitch/L3.1-70b-Inori"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Downloads last month: 24

Safetensors

Model size

71B params

Tensor type

BF16

Model tree for KaraKaraWarehouse/L3.1-70b-Inori

Fizzarolli/L3.1-70b-glitz-v0.2

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

abacusai/Dracarys-Llama-3.1-70B-Instruct

cyberagent/Llama-3.1-70B-Japanese-Instruct-2407

gbueno86/Cathallama-70B

nothingiisreal/L3.1-70B-Celeste-V0.1-BF16

sophosympatheia/New-Dawn-Llama-3.1-70B-v1.1

Merge model

this model

Quantizations

3 models