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", 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 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
```

Legion-V2.1-LLaMa-70B / README.md

Tarek07

Update README.md

883ac6c verified over 1 year ago

preview code

Raw

History Blame Contribute Delete

3.45 kB

	---
	base_model:
	- TareksLab/L2-MERGE4
	- TareksLab/L2-MERGE1
	- TareksLab/L2-MERGE3
	- TareksLab/L2-MERGE2a
	- TareksLab/L-BASE-V1
	library_name: transformers
	tags:
	- mergekit
	- merge
	license: llama3.3
	---
	~ We are Legion...

	![image/png](https://cdn-uploads.huggingface.co/production/uploads/64909c086073a0cd172d0411/mqajIk-EsgQ0ZVAZJ4trP.png)

	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](https://ko-fi.com/tarek07)

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [TareksLab/L-BASE-V1](https://huggingface.co/TareksLab/L-BASE-V1) as a base.

	### Models Merged

	The following models were included in the merge:
	* [TareksLab/L2-MERGE4](https://huggingface.co/TareksLab/L2-MERGE4)
	* [TareksLab/L2-MERGE1](https://huggingface.co/TareksLab/L2-MERGE1)
	* [TareksLab/L2-MERGE3](https://huggingface.co/TareksLab/L2-MERGE3)
	* [TareksLab/L2-MERGE2a](https://huggingface.co/TareksLab/L2-MERGE2a)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	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
	```