Instructions to use ohyeah1/Human-like-nemo-light with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ohyeah1/Human-like-nemo-light with Transformers:

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

pipe = pipeline("text-generation", model="ohyeah1/Human-like-nemo-light")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ohyeah1/Human-like-nemo-light")
model = AutoModelForCausalLM.from_pretrained("ohyeah1/Human-like-nemo-light", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ohyeah1/Human-like-nemo-light with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ohyeah1/Human-like-nemo-light"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ohyeah1/Human-like-nemo-light",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ohyeah1/Human-like-nemo-light

SGLang

How to use ohyeah1/Human-like-nemo-light 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 "ohyeah1/Human-like-nemo-light" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ohyeah1/Human-like-nemo-light",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "ohyeah1/Human-like-nemo-light" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ohyeah1/Human-like-nemo-light",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ohyeah1/Human-like-nemo-light with Docker Model Runner:
```
docker model run hf.co/ohyeah1/Human-like-nemo-light
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Human-like model. A good tradeoff between intelligence and human-like

The following YAML configuration was used to produce this model:

models:
  - model: Francois-PE-12B
    parameters:
      weight: 0.3
      density: 0.4
  - model: Nera_Noctis-12B
    parameters:
      weight: 0.3
      density: 0.4
  - model: BlueLight-12B
    parameters:
      weight: 0.5
      density: 0.8
base_model: Mistral-Nemo-Base-2407
parameters:
  lambda: 1.0
  epsilon: 0.1
  rescale: true
  normalize: false
  int8_mask: true
merge_method: della_linear
tokenizer:
  source: union
dtype: bfloat16

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Safetensors

Model size

12B params

Tensor type

BF16

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Quantizations

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