Instructions to use NexesMess/Llama_3.x_70b_Dolmen_v1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NexesMess/Llama_3.x_70b_Dolmen_v1.0 with Transformers:

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

pipe = pipeline("text-generation", model="NexesMess/Llama_3.x_70b_Dolmen_v1.0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("NexesMess/Llama_3.x_70b_Dolmen_v1.0")
model = AutoModelForMultimodalLM.from_pretrained("NexesMess/Llama_3.x_70b_Dolmen_v1.0")
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 NexesMess/Llama_3.x_70b_Dolmen_v1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NexesMess/Llama_3.x_70b_Dolmen_v1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NexesMess/Llama_3.x_70b_Dolmen_v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NexesMess/Llama_3.x_70b_Dolmen_v1.0

SGLang

How to use NexesMess/Llama_3.x_70b_Dolmen_v1.0 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 "NexesMess/Llama_3.x_70b_Dolmen_v1.0" \
    --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": "NexesMess/Llama_3.x_70b_Dolmen_v1.0",
		"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 "NexesMess/Llama_3.x_70b_Dolmen_v1.0" \
        --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": "NexesMess/Llama_3.x_70b_Dolmen_v1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use NexesMess/Llama_3.x_70b_Dolmen_v1.0 with Docker Model Runner:
```
docker model run hf.co/NexesMess/Llama_3.x_70b_Dolmen_v1.0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

about

Original name : Llama_3.x_70b_Dolnemhertulimtess_v1.0

Also known as : Llama_3.x_70b_Dolmen_v1.0 (1.1 will come soon)

This model is essentially a Llama 3.1 smart brick based on by a 3.0->3.3 "port", to be used in second level merges.

This time, for the base, I used a Llama 3.0 Dolphin 2.9.1/Llama 3.3 instruct abliterated merge, in order to get both the capabilities of each model, and notably Dolphin, not ported on Llama 70b 3.1 or 3.3 by CognitiveComputations.

Then, I added the best 'instructions oriented' finetunes I know, simple as that.

The model is highly uncensored, quite intelligent, and can be used as a standalone.

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 Nexesenex/Llama_3.x_70b_L3.3_Dolphin_128K_v1.02 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: Nexesenex/Llama_3.x_70b_L3.3_Dolphin_128K_v1.02
    parameters:
      weight: 1.0
  - model: nbeerbower/Llama-3.1-Nemotron-lorablated-70B
    parameters:
      weight: 1.0
  - model: mlabonne/Hermes-3-Llama-3.1-70B-lorablated
    parameters:
      weight: 1.0
  - model: huihui-ai/Llama-3.1-Tulu-3-70B-abliterated
    parameters:
      weight: 1.0
  - model: huihui-ai/Tess-R1-Limerick-Llama-3.1-70B-abliterated
    parameters:
      weight: 1.0
  - model: migtissera/Tess-3-Llama-3.1-70B
    parameters:
      weight: 1.0
base_model: Nexesenex/Llama_3.x_70b_L3.3_Dolphin_128K_v1.02
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