Instructions to use speechlessai/speechless-mistral-7b-dare-0.85 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use speechlessai/speechless-mistral-7b-dare-0.85 with Transformers:

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

pipe = pipeline("text-generation", model="speechlessai/speechless-mistral-7b-dare-0.85")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("speechlessai/speechless-mistral-7b-dare-0.85")
model = AutoModelForCausalLM.from_pretrained("speechlessai/speechless-mistral-7b-dare-0.85")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use speechlessai/speechless-mistral-7b-dare-0.85 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "speechlessai/speechless-mistral-7b-dare-0.85"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "speechlessai/speechless-mistral-7b-dare-0.85",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/speechlessai/speechless-mistral-7b-dare-0.85

SGLang

How to use speechlessai/speechless-mistral-7b-dare-0.85 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 "speechlessai/speechless-mistral-7b-dare-0.85" \
    --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": "speechlessai/speechless-mistral-7b-dare-0.85",
		"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 "speechlessai/speechless-mistral-7b-dare-0.85" \
        --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": "speechlessai/speechless-mistral-7b-dare-0.85",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use speechlessai/speechless-mistral-7b-dare-0.85 with Docker Model Runner:
```
docker model run hf.co/speechlessai/speechless-mistral-7b-dare-0.85
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Experiment for DARE(Drop and REscale), most of the delta parameters can be directly set to zeros without affecting the capabilities of SFT LMs and larger models can tolerate a higher proportion of discarded parameters.

Merged with below DARE models.

weight_mask_rate: 0.85 / use_weight_rescale: True / mask_stratery: random / scaling_coefficient: 1.0

Model	Average	ARC	HellaSwag	MMLU	TruthfulQA	Winogrande	GSM8K
Intel/neural-chat-7b-v3-1	61.59	66.21	83.64	62.37	59.65	78.14	19.56
migtissera/SynthIA-7B-v1.3	59.34	62.12	83.45	62.65	51.37	78.85	17.59
bhenrym14/mistral-7b-platypus-fp16	58.71	63.05	84.15	64.11	45.07	78.53	17.36
jondurbin/airoboros-m-7b-3.1.2	58.75	61.86	83.51	61.91	53.75	77.58	13.87
teknium/CollectiveCognition-v1.1-Mistral-7B	62.92	62.12	84.17	62.35	57.62	75.37	15.62
uukuguy/speechless-mistral-dolphin-orca-platypus-samantha-7b	62.06	64.33	84.4	63.72	52.52	78.37	21.38

speechless-mistral-7b-dare-0.85 (Merge 6 DARE models)	64.69	63.57	84.82	64.29	50.66	79.24	45.56

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Safetensors

Model size

7B params

Tensor type

F32

BF16

Model tree for speechlessai/speechless-mistral-7b-dare-0.85

Quantizations

4 models