Instructions to use Locutusque/Rhino-Mistral-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Locutusque/Rhino-Mistral-7B with Transformers:

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

pipe = pipeline("text-generation", model="Locutusque/Rhino-Mistral-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Locutusque/Rhino-Mistral-7B")
model = AutoModelForCausalLM.from_pretrained("Locutusque/Rhino-Mistral-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Locutusque/Rhino-Mistral-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Locutusque/Rhino-Mistral-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/Rhino-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Locutusque/Rhino-Mistral-7B

SGLang

How to use Locutusque/Rhino-Mistral-7B 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 "Locutusque/Rhino-Mistral-7B" \
    --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": "Locutusque/Rhino-Mistral-7B",
		"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 "Locutusque/Rhino-Mistral-7B" \
        --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": "Locutusque/Rhino-Mistral-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Locutusque/Rhino-Mistral-7B with Docker Model Runner:
```
docker model run hf.co/Locutusque/Rhino-Mistral-7B
```

Model Card for Model ID

This model aims to be a high-performance chatbot. During training, examples that have a quality score of less than 0.03 are skipped.

Model Details

Model Description

This model is to be used as a general-purpose chatbot/assistant. Trained on about 400,000 examples of M4-ai/Rhino, examples with a quality score lower than 0.03 are removed. During validation, this model achieved a loss of 0.55

This model was trained on the ChatML prompt format.

Developed by: Locutusque
Model type: mistral
Language(s) (NLP): English
License: cc-by-nc-4.0
Finetuned from model: mistralai/Mistral-7B-v0.1

Uses

This model is to be used as a general-purpose assistant, and may need to be further fine-tuned on DPO to detoxify the model or SFT for a more specific task.

Direct Use

This model should be used as a general assistant. This model is capable of writing code, answering questions, and following instructions.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

Training Details

Training Hyperparameters

Training regime: bf16 non-mixed precision

Evaluation

Testing Data, Factors & Metrics

Testing Data

First 100 examples of M4-ai/Rhino. Training data does not include these examples.

Results

Test loss - 0.48

Summary

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: 8 TPU V3s
Hours used: 7
Cloud Provider: Kaggle
Compute Region: [More Information Needed]
Carbon Emitted: 8.88

Downloads last month: 10

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Locutusque/Rhino-Mistral-7B

Base model

mistralai/Mistral-7B-v0.1

Finetuned

(1033)

this model

Dataset used to train Locutusque/Rhino-Mistral-7B

Paper for Locutusque/Rhino-Mistral-7B

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 45