Instructions to use CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2")
model = AutoModelForMultimodalLM.from_pretrained("CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2")
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 CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2

SGLang

How to use CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2 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 "CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2" \
    --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": "CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2",
		"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 "CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2" \
        --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": "CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2 with Docker Model Runner:
```
docker model run hf.co/CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2
```

mistral-nemo-gutenberg-12B-v4-exl2

This repository contains various EXL2 quantisations of nbeerbower/mistral-nemo-gutenberg-12B-v4.

Quantisations available:

Branch	Description	Recommended
2.0-bpw	2 bits per weight	Low Quality - Smallest Available Quantisation
3.0-bpw	3 bits per weight
4.0-bpw	4 bits per weight	✔️ - Recommended for Low-VRAM Environments
5.0-bpw	5 bits per weight
6.0-bpw	6 bits per weight	✔️ - Best Quality / VRAM Balance
6.5-bpw	6.5 bits per weight	✔️ - Near Perfect Quality, Slightly Higher VRAM Usage
8.0-bpw	8.0 bits per weight	Best Available Quality - Almost always unnecessary

ORIGINAL README:

TheDrummer/Rocinante-12B-v1 finetuned on jondurbin/gutenberg-dpo-v0.1.

Method

Finetuned using an A100 on Google Colab for 3 epochs.

Fine-tune Llama 3 with ORPO

Downloads last month: 7

Model tree for CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2

Base model

TheDrummer/Rocinante-12B-v1

Finetuned

(2)

this model

CameronRedmore
/

mistral-nemo-gutenberg-12B-v4-exl2

mistral-nemo-gutenberg-12B-v4-exl2

ORIGINAL README:

Method

Model tree for CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2

Dataset used to train CameronRedmore/mistral-nemo-gutenberg-12B-v4-exl2