Instructions to use DS-Archive/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DS-Archive/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES with Transformers:

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

pipe = pipeline("text-generation", model="DS-Archive/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DS-Archive/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES")
model = AutoModelForCausalLM.from_pretrained("DS-Archive/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use DS-Archive/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/DS-Archive/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES

SGLang

How to use DS-Archive/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES 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 "DS-Archive/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES" \
    --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": "DS-Archive/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES",
		"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 "DS-Archive/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES" \
        --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": "DS-Archive/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use DS-Archive/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES with Docker Model Runner:
```
docker model run hf.co/DS-Archive/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using ./extra_hdd/Mixtral-8x7B-v0.1 as a base.

Models Merged

The following models were included in the merge:

./extra_hdd2/Mixtral-8x7B-Instruct-v0.1
./extra_hdd/Mixtral-8x7B-v0.1-LimaRP-ZLoss

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: ./extra_hdd2/Mixtral-8x7B-Instruct-v0.1
    parameters:
      density: 0.5
      weight: 1.0
  - model: ./extra_hdd/Mixtral-8x7B-v0.1-LimaRP-ZLoss
    parameters:
      density: 0.5
      weight: 0.5
merge_method: dare_ties
base_model: ./extra_hdd/Mixtral-8x7B-v0.1
parameters:
  #normalize: false
  #int8_mask: true
dtype: bfloat16

Downloads last month: 3

Safetensors

Model size

47B params

Tensor type

BF16

Model tree for DS-Archive/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES

Quantizations

3 models

Papers for DS-Archive/Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss-DARE-TIES

Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch

Paper • 2311.03099 • Published Nov 6, 2023 • 35

Resolving Interference When Merging Models

Paper • 2306.01708 • Published Jun 2, 2023 • 19