Instructions to use Nexesenex/Llama_3.x_70b_Hexagon_Blue_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_Blue_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_Blue_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_Blue_V1")
model = AutoModelForCausalLM.from_pretrained("Nexesenex/Llama_3.x_70b_Hexagon_Blue_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

vLLM

How to use Nexesenex/Llama_3.x_70b_Hexagon_Blue_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_Blue_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_Blue_V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nexesenex/Llama_3.x_70b_Hexagon_Blue_V1

SGLang

How to use Nexesenex/Llama_3.x_70b_Hexagon_Blue_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_Blue_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_Blue_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_Blue_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_Blue_V1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nexesenex/Llama_3.x_70b_Hexagon_Blue_V1 with Docker Model Runner:
```
docker model run hf.co/Nexesenex/Llama_3.x_70b_Hexagon_Blue_V1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

about

Smatricks_v1.30_flat + DarkHorse for a darker colloration.

I started to use it, and I'm satisfied.

In the line of the Smartricks v1.30 flat, its prose is quite different of what I usually observe in my merges based on a smart merge.
The addition of DarkHorse brings more creativity without damaging much the smarts.
I continue to think that 2 levels of merges is quite optimal for a final model when using merge_stock. Beyond, it becomes more "soupy".

benchs

IK_LLama.CPP Benchs in IQ6_K:

PPL-512 WikiEng Text 564 : 3.40
ARC-C 299 : 59.87
ARC-E 570 : 81.23
Hellaswag 200 : 86.5
Winogrande 1263 : 81.92
MMLU : 46.90

MMLU results are much lower than they should be on LlamaCPP. This is a constant quirk since 2024.

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 huihui-ai/Llama-3.3-70B-Instruct-abliterated 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: TheDrummer/Fallen-Llama-3.3-R1-70B-v1
    parameters:
      weight: 1.0
  - model: Nexesenex/Llama_3.3_70b_DarkHorse
    parameters:
      weight: 1.0
  - model: huihui-ai/Llama-3.1-Nemotron-70B-Instruct-HF-abliterated
    parameters:
      weight: 1.0
  - model: huihui-ai/Llama-3.1-Tulu-3-70B-abliterated
    parameters:
      weight: 1.0
  - model: hitachi-nlp/Llama-3.1-70B-FLDx2
    parameters:
      weight: 1.0
base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated
dtype: bfloat16
out_dtype: bfloat16
parameters:
  int8_mask: true
  normalize: true
  rescale: false
  filter_wise: false
  smooth: false
  allow_negative_weights: false
chat_template: auto
tokenizer:
  source: union