Instructions to use DrNicefellow/Mistral-7-from-Mixtral-8x7B-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DrNicefellow/Mistral-7-from-Mixtral-8x7B-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="DrNicefellow/Mistral-7-from-Mixtral-8x7B-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("DrNicefellow/Mistral-7-from-Mixtral-8x7B-v0.1")
model = AutoModelForMultimodalLM.from_pretrained("DrNicefellow/Mistral-7-from-Mixtral-8x7B-v0.1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DrNicefellow/Mistral-7-from-Mixtral-8x7B-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DrNicefellow/Mistral-7-from-Mixtral-8x7B-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DrNicefellow/Mistral-7-from-Mixtral-8x7B-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/DrNicefellow/Mistral-7-from-Mixtral-8x7B-v0.1

SGLang

How to use DrNicefellow/Mistral-7-from-Mixtral-8x7B-v0.1 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 "DrNicefellow/Mistral-7-from-Mixtral-8x7B-v0.1" \
    --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": "DrNicefellow/Mistral-7-from-Mixtral-8x7B-v0.1",
		"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 "DrNicefellow/Mistral-7-from-Mixtral-8x7B-v0.1" \
        --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": "DrNicefellow/Mistral-7-from-Mixtral-8x7B-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DrNicefellow/Mistral-7-from-Mixtral-8x7B-v0.1 with Docker Model Runner:
```
docker model run hf.co/DrNicefellow/Mistral-7-from-Mixtral-8x7B-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mixtral-8x7B--v0.1: Model 7

Model Description

This model is the 7th extracted standalone model from the mistralai/Mixtral-8x7B-v0.1, using the Mixtral Model Expert Extractor tool I made. It is constructed by selecting the first expert from each Mixture of Experts (MoE) layer. The extraction of this model is experimental. It is expected to be worse than Mistral-7B.

Model Architecture

The architecture of this model includes:

Multi-head attention layers derived from the base Mixtral model.
The first expert from each MoE layer, intended to provide a balanced approach to language understanding and generation tasks.
Additional layers and components as required to ensure the model's functionality outside the MoE framework.

Example

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "DrNicefellow/Mistral-3-from-Mixtral-8x7B-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

text = "Today is a pleasant"
input_ids = tokenizer.encode(text, return_tensors='pt')
output = model.generate(input_ids)

print(tokenizer.decode(output[0], skip_special_tokens=True))

License

This model is available under the Apache 2.0 License.

Discord Server

Join our Discord server here.

License

This model is open-sourced under the Apache 2.0 License. See the LICENSE file for more details.

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Safetensors

Model size

7B params

Tensor type

BF16

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