Instructions to use Tarek07/Legion-V2.1-LLaMa-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tarek07/Legion-V2.1-LLaMa-70B with Transformers:

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

pipe = pipeline("text-generation", model="Tarek07/Legion-V2.1-LLaMa-70B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Tarek07/Legion-V2.1-LLaMa-70B")
model = AutoModelForCausalLM.from_pretrained("Tarek07/Legion-V2.1-LLaMa-70B")
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

vLLM

How to use Tarek07/Legion-V2.1-LLaMa-70B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tarek07/Legion-V2.1-LLaMa-70B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tarek07/Legion-V2.1-LLaMa-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tarek07/Legion-V2.1-LLaMa-70B

SGLang

How to use Tarek07/Legion-V2.1-LLaMa-70B 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 "Tarek07/Legion-V2.1-LLaMa-70B" \
    --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": "Tarek07/Legion-V2.1-LLaMa-70B",
		"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 "Tarek07/Legion-V2.1-LLaMa-70B" \
        --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": "Tarek07/Legion-V2.1-LLaMa-70B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tarek07/Legion-V2.1-LLaMa-70B with Docker Model Runner:
```
docker model run hf.co/Tarek07/Legion-V2.1-LLaMa-70B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

~ We are Legion...

My biggest merge yet, consisting of a total of 20 specially curated models. My methodology in approaching this was to create 5 highly specialized models:

A completely uncensored base
A very intelligent model based on UGI, Willingness and NatInt scores on the UGI Leaderboard
A highly descriptive writing model, specializing in creative and natural prose
A RP model specially merged with fine-tuned models that use a lot of RP datasets
The secret ingredient: A completely unhinged, uncensored final model

These five models went through a series of iterations until I got something I thought worked well and then combined them to make LEGION.

The full list of models used in this merge is below:

TheDrummer/Fallen-Llama-3.3-R1-70B-v1
Sao10K/Llama-3.3-70B-Vulpecula-r1
Sao10K/L3-70B-Euryale-v2.1
SicariusSicariiStuff/Negative_LLAMA_70B
allura-org/Bigger-Body-70b
Sao10K/70B-L3.3-mhnnn-x1
Sao10K/L3.3-70B-Euryale-v2.3
Doctor-Shotgun/L3.3-70B-Magnum-v4-SE
Sao10K/L3.1-70B-Hanami-x1
Sao10K/70B-L3.3-Cirrus-x1
EVA-UNIT-01/EVA-LLaMA-3.33-70B-v0.1
TheDrummer/Anubis-70B-v1
ArliAI/Llama-3.3-70B-ArliAI-RPMax-v1.4
LatitudeGames/Wayfarer-Large-70B-Llama-3.3
NeverSleep/Lumimaid-v0.2-70B
mlabonne/Hermes-3-Llama-3.1-70B-lorablated
ReadyArt/Forgotten-Safeword-70B-3.6
ReadyArt/Fallen-Abomination-70B-R1-v4.1
ReadyArt/Fallen-Safeword-70B-R1-v4.1
huihui-ai/Llama-3.3-70B-Instruct-abliterated

Recommended settings:

Temp 1.0
Min P 0.02

Because of the nature of this sort of 'Hyper Multi Model Merge', my recommendation is not to run this on anything lower than a Q5 quant.

If you enjoy my work, please consider supporting me, It helps me make more models like this! Support on KO-FI <3

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

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using TareksLab/L-BASE-V1 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: TareksLab/L2-MERGE2a
    parameters:
      weight: 0.20
      density: 0.5
  - model: TareksLab/L2-MERGE4
    parameters:
      weight: 0.20
      density: 0.5
  - model: TareksLab/L-BASE-V1
    parameters:
      weight: 0.20
      density: 0.5
  - model: TareksLab/L2-MERGE3
    parameters:
      weight: 0.20
      density: 0.5
  - model: TareksLab/L2-MERGE1
    parameters:
      weight: 0.20
      density: 0.5
merge_method: dare_ties
base_model: TareksLab/L-BASE-V1
parameters:
  normalize: false
out_dtype: bfloat16
chat_template: llama3
tokenizer:
 source: base