Instructions to use Nexesenex/Llama_3.x_70b_Dolmen_v1.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nexesenex/Llama_3.x_70b_Dolmen_v1.2 with Transformers:

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

pipe = pipeline("text-generation", model="Nexesenex/Llama_3.x_70b_Dolmen_v1.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Nexesenex/Llama_3.x_70b_Dolmen_v1.2")
model = AutoModelForMultimodalLM.from_pretrained("Nexesenex/Llama_3.x_70b_Dolmen_v1.2")
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_Dolmen_v1.2 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_Dolmen_v1.2"
# 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_Dolmen_v1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Nexesenex/Llama_3.x_70b_Dolmen_v1.2

SGLang

How to use Nexesenex/Llama_3.x_70b_Dolmen_v1.2 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_Dolmen_v1.2" \
    --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_Dolmen_v1.2",
		"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_Dolmen_v1.2" \
        --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_Dolmen_v1.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Nexesenex/Llama_3.x_70b_Dolmen_v1.2 with Docker Model Runner:
```
docker model run hf.co/Nexesenex/Llama_3.x_70b_Dolmen_v1.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

about

Original name : NexesMess/Llama_3.x_70b_Dolnemlimwhitessachi_v1.0

Release name : Dolmen v1.2

Replacing : https://huggingface.co/NexesMess/Llama_3.x_70b_Dolnemhertulimtess_v1.0 (Dolmen v1.0)

changes

OUT :

Tulu goes out, too "messy" and somehow "shackling" the model.
Hermes goes out, despite its quality, due to its tokenizer with a differt EOS token. I had too much problems with that model.

IN :

Hitachi FLDx2 enters, for intelligence and low perplexity (it's my new champion, PPL wikitext Eng 512 2.84 instead of 2.92), joining Tess 3 in those roles.
WhiteRabbitNeo enters, as a stabilizator.

feel

There's definitively progress. The prose is more structured, and the EOS is now working.

credits

Blackroot, for his own observations on his Mirai Series, and the hint about the Hitachi model, which I then tested with delight.

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: huihui-ai/Llama-3.1-Nemotron-70B-Instruct-HF-abliterated
    parameters:
      weight: 1.0
  - model: huihui-ai/Tess-R1-Limerick-Llama-3.1-70B-abliterated
    parameters:
      weight: 1.0
  - model: WhiteRabbitNeo/Llama-3.1-WhiteRabbitNeo-2-70B
    parameters:
      weight: 1.0
  - model: migtissera/Tess-3-Llama-3.1-70B
    parameters:
      weight: 1.0
  - model: hitachi-nlp/Llama-3.1-70B-FLDx2 
    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