Instructions to use Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1 with Transformers:

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

pipe = pipeline("text-generation", model="Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1")
model = AutoModelForCausalLM.from_pretrained("Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1")
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 Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1

SGLang

How to use Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1 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 "Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1" \
    --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": "Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1",
		"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 "Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1" \
        --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": "Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1 with Docker Model Runner:
```
docker model run hf.co/Nexesenex/Llama_3.x_70b_Hexagon_Pink_V1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

about

Changes from Hexagon Purple V2 :

Electra becomes base and lead model.
ReadyAct Forgotten Safeword enters and go second to unhinge the model. A bet, hence Hexagon Pink.
Smarteaz goes out, I will make a new version of my "smart merge" soon enough and Electra will take over the smarts for now.
Priestess becomes HighPriestess, Lumitron is back within it.

What stays :

DoppelGanger R1 stays, to keep reinforcing the R1 skills and bring Dobby's personnality and more of Wayfarer.
Gutemberg Doppel stays, for Hermes' smarts and writing skills.
Tess stays, as the perplexity dropper.

benchs

ARC-C : 58.85 (average+) ARC-E : 82.65 (very good) PPL 512 Wikitext Eng : 3.28 (very good)

merge

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

Merge Details

Merge Method

This model was merged using the Model Stock merge method using Steelskull/L3.3-Electra-R1-70b as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

merge_method: model_stock
models:
  - model: Steelskull/L3.3-Electra-R1-70b
    parameters:
      weight: 1.0
  - model: Strangedove/ReadyArt_Forgotten-Safeword-70B-3.6-EmbedFix
    parameters:
      weight: 1.0
  - model: NexesMess/Llama_3.3_70b_DoppelGanger_R1
    parameters:
      weight: 1.0
  - model: Nexesenex/Llama_3.1_70b_HighPriestess_R1_V1
    parameters:
      weight: 1.0
  - model: nbeerbower/Llama3.1-Gutenberg-Doppel-70B
    parameters:
      weight: 1.0
  - model: migtissera/Tess-3-Llama-3.1-70B
    parameters:
      weight: 1.0
base_model: Steelskull/L3.3-Electra-R1-70b
dtype: bfloat16
out_dtype: bfloat16
parameters:
  int8_mask: true
  normalize: true
  rescale: false
chat_template: auto
tokenizer:
  source: union