Instructions to use NovoCode/Mistral-NeuralDPO-v0.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NovoCode/Mistral-NeuralDPO-v0.3 with Transformers:

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

pipe = pipeline("text-generation", model="NovoCode/Mistral-NeuralDPO-v0.3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NovoCode/Mistral-NeuralDPO-v0.3")
model = AutoModelForCausalLM.from_pretrained("NovoCode/Mistral-NeuralDPO-v0.3")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use NovoCode/Mistral-NeuralDPO-v0.3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NovoCode/Mistral-NeuralDPO-v0.3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NovoCode/Mistral-NeuralDPO-v0.3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NovoCode/Mistral-NeuralDPO-v0.3

SGLang

How to use NovoCode/Mistral-NeuralDPO-v0.3 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 "NovoCode/Mistral-NeuralDPO-v0.3" \
    --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": "NovoCode/Mistral-NeuralDPO-v0.3",
		"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 "NovoCode/Mistral-NeuralDPO-v0.3" \
        --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": "NovoCode/Mistral-NeuralDPO-v0.3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NovoCode/Mistral-NeuralDPO-v0.3 with Docker Model Runner:
```
docker model run hf.co/NovoCode/Mistral-NeuralDPO-v0.3
```

Adding Evaluation Results

by leaderboard-pr-bot - opened Mar 4, 2024

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+15

-1

Files changed (1) hide show

README.md +15 -1

README.md CHANGED Viewed

@@ -1,8 +1,8 @@
 ---
 license: apache-2.0
-base_model: mistralai/Mistral-7B-v0.1
 tags:
 - generated_from_trainer
 model-index:
 - name: out
   results: []
@@ -134,3 +134,17 @@ The following hyperparameters were used during training:
 - Pytorch 2.2.0+cu121
 - Datasets 2.17.0
 - Tokenizers 0.15.0

 ---
 license: apache-2.0
 tags:
 - generated_from_trainer
+base_model: mistralai/Mistral-7B-v0.1
 model-index:
 - name: out
   results: []
 - Pytorch 2.2.0+cu121
 - Datasets 2.17.0
 - Tokenizers 0.15.0
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_NovoCode__Mistral-NeuralDPO-v0.3)
+|             Metric              |Value|
+|---------------------------------|----:|
+|Avg.                             |60.75|
+|AI2 Reasoning Challenge (25-Shot)|61.60|
+|HellaSwag (10-Shot)              |83.15|
+|MMLU (5-Shot)                    |61.60|
+|TruthfulQA (0-shot)              |45.31|
+|Winogrande (5-shot)              |77.98|
+|GSM8k (5-shot)                   |34.87|