Instructions to use allura-forge/nemo-upscaled-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allura-forge/nemo-upscaled-1 with Transformers:

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

pipe = pipeline("text-generation", model="allura-forge/nemo-upscaled-1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("allura-forge/nemo-upscaled-1")
model = AutoModelForCausalLM.from_pretrained("allura-forge/nemo-upscaled-1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use allura-forge/nemo-upscaled-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allura-forge/nemo-upscaled-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allura-forge/nemo-upscaled-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/allura-forge/nemo-upscaled-1

SGLang

How to use allura-forge/nemo-upscaled-1 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 "allura-forge/nemo-upscaled-1" \
    --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": "allura-forge/nemo-upscaled-1",
		"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 "allura-forge/nemo-upscaled-1" \
        --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": "allura-forge/nemo-upscaled-1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use allura-forge/nemo-upscaled-1 with Docker Model Runner:
```
docker model run hf.co/allura-forge/nemo-upscaled-1
```

out-2

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

Merge Details

Merge Method

This model was merged using the Passthrough merge method.

Models Merged

The following models were included in the merge:

mistralai/Mistral-Nemo-Base-2407

Configuration

The following YAML configuration was used to produce this model:

dtype: bfloat16
merge_method: passthrough

slices:
  # untouched intro
  - sources:
      - layer_range: [0, 8]
        model: mistralai/Mistral-Nemo-Base-2407

  - sources:
      - layer_range: [8, 12]
        model: mistralai/Mistral-Nemo-Base-2407
  # 8–16 baseline
  - sources:
      - layer_range: [8, 16]
        model: mistralai/Mistral-Nemo-Base-2407
  # 8–16 duplicate with projections nulled
  - sources:
      - layer_range: [8, 16]
        model: mistralai/Mistral-Nemo-Base-2407
        parameters:
          scale:
            - filter: o_proj
              value: 0.0
            - filter: down_proj
              value: 0.0
            - value: 1.0

  # 16–24 duplicate
  - sources:
      - layer_range: [16, 24]
        model: mistralai/Mistral-Nemo-Base-2407
        parameters:
          scale:
            - filter: o_proj
              value: 0.0
            - filter: down_proj
              value: 0.0
            - value: 1.0
  # 16–24 baseline
  - sources:
      - layer_range: [16, 24]
        model: mistralai/Mistral-Nemo-Base-2407
  # 16–24 duplicate
  - sources:
      - layer_range: [16, 24]
        model: mistralai/Mistral-Nemo-Base-2407
        parameters:
          scale:
            - filter: o_proj
              value: 0.0
            - filter: down_proj
              value: 0.0
            - value: 1.0

  # 24–32 baseline
  - sources:
      - layer_range: [24, 32]
        model: mistralai/Mistral-Nemo-Base-2407
  # 24–32 duplicate
  - sources:
      - layer_range: [24, 32]
        model: mistralai/Mistral-Nemo-Base-2407
        parameters:
          scale:
            - filter: o_proj
              value: 0.0
            - filter: down_proj
              value: 0.0
            - value: 1.0

  # untouched tail
  - sources:
      - layer_range: [32, 40]
        model: mistralai/Mistral-Nemo-Base-2407

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Safetensors

Model size

22B params

Tensor type

BF16

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