Instructions to use RESMPDEV/Wukong-0.1-Mistral-7B-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RESMPDEV/Wukong-0.1-Mistral-7B-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="RESMPDEV/Wukong-0.1-Mistral-7B-v0.2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RESMPDEV/Wukong-0.1-Mistral-7B-v0.2")
model = AutoModelForCausalLM.from_pretrained("RESMPDEV/Wukong-0.1-Mistral-7B-v0.2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use RESMPDEV/Wukong-0.1-Mistral-7B-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RESMPDEV/Wukong-0.1-Mistral-7B-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RESMPDEV/Wukong-0.1-Mistral-7B-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RESMPDEV/Wukong-0.1-Mistral-7B-v0.2

SGLang

How to use RESMPDEV/Wukong-0.1-Mistral-7B-v0.2 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 "RESMPDEV/Wukong-0.1-Mistral-7B-v0.2" \
    --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": "RESMPDEV/Wukong-0.1-Mistral-7B-v0.2",
		"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 "RESMPDEV/Wukong-0.1-Mistral-7B-v0.2" \
        --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": "RESMPDEV/Wukong-0.1-Mistral-7B-v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RESMPDEV/Wukong-0.1-Mistral-7B-v0.2 with Docker Model Runner:
```
docker model run hf.co/RESMPDEV/Wukong-0.1-Mistral-7B-v0.2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Wukong-0.1-Mistral-7B-v0.2

Join Our Discord! https://discord.gg/cognitivecomputations

Wukong-0.1-Mistral-7B-v0.2 is a dealigned chat finetune of the original fantastic Mistral-7B-v0.2 model by the Mistral team.

This model was trained on the teknium OpenHeremes-2.5 dataset, code datasets from Multimodal Art Projection https://m-a-p.ai, and the Dolphin dataset from Cognitive Computations https://erichartford.com/dolphin 🐬

This model was trained for 3 epochs over 4 4090's.

Example Outputs

TBD

Downloads last month: 3

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for RESMPDEV/Wukong-0.1-Mistral-7B-v0.2

Quantizations

3 models

RESMPDEV
/

Wukong-0.1-Mistral-7B-v0.2

Wukong-0.1-Mistral-7B-v0.2

Example Outputs

Model tree for RESMPDEV/Wukong-0.1-Mistral-7B-v0.2

Datasets used to train RESMPDEV/Wukong-0.1-Mistral-7B-v0.2