Instructions to use ConicCat/Nemo-Humanities with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ConicCat/Nemo-Humanities with Transformers:

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

pipe = pipeline("text-generation", model="ConicCat/Nemo-Humanities")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ConicCat/Nemo-Humanities")
model = AutoModelForCausalLM.from_pretrained("ConicCat/Nemo-Humanities")
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 ConicCat/Nemo-Humanities with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ConicCat/Nemo-Humanities

SGLang

How to use ConicCat/Nemo-Humanities 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 "ConicCat/Nemo-Humanities" \
    --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": "ConicCat/Nemo-Humanities",
		"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 "ConicCat/Nemo-Humanities" \
        --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": "ConicCat/Nemo-Humanities",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

A finetune of nemo produced to test the theory that because the humanities (writing, social intelligence, roleplay, and theory of mind) seem to cross generalize, training all of them at once is superior to focusing on any one field.

Downloads last month: 3

Safetensors

Model size

12B params

Tensor type

BF16

Model tree for ConicCat/Nemo-Humanities

Base model

mistralai/Mistral-Nemo-Base-2407

Finetuned

mistralai/Mistral-Nemo-Instruct-2407

Finetuned

(209)

this model

Quantizations

3 models