Instructions to use FredrikBL/FlashbackMist-dare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FredrikBL/FlashbackMist-dare with Transformers:

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

pipe = pipeline("text-generation", model="FredrikBL/FlashbackMist-dare")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FredrikBL/FlashbackMist-dare")
model = AutoModelForCausalLM.from_pretrained("FredrikBL/FlashbackMist-dare", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FredrikBL/FlashbackMist-dare with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/FredrikBL/FlashbackMist-dare

SGLang

How to use FredrikBL/FlashbackMist-dare 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 "FredrikBL/FlashbackMist-dare" \
    --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": "FredrikBL/FlashbackMist-dare",
		"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 "FredrikBL/FlashbackMist-dare" \
        --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": "FredrikBL/FlashbackMist-dare",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FredrikBL/FlashbackMist-dare with Docker Model Runner:
```
docker model run hf.co/FredrikBL/FlashbackMist-dare
```

FlashbackMist-dare / mergekit_config.yml

FredrikBL

Upload folder using huggingface_hub

6f49091 verified over 2 years ago

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496 Bytes

models:
  - model: mistralai/Mistral-7B-v0.1
    # No parameters necessary for base model
  - model: timpal0l/Mistral-7B-v0.1-flashback-v2
    parameters:
      density: 0.53
      weight: 0.4
  - model: abacusai/Slerp-CM-mist-dpo
    parameters:
      density: 0.53
      weight: 0.3
  - model: EmbeddedLLM/Mistral-7B-Merge-14-v0.2
    parameters:
      density: 0.53
      weight: 0.3
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
dtype: bfloat16


	models:
	- model: mistralai/Mistral-7B-v0.1
	# No parameters necessary for base model
	- model: timpal0l/Mistral-7B-v0.1-flashback-v2
	parameters:
	density: 0.53
	weight: 0.4
	- model: abacusai/Slerp-CM-mist-dpo
	parameters:
	density: 0.53
	weight: 0.3
	- model: EmbeddedLLM/Mistral-7B-Merge-14-v0.2
	parameters:
	density: 0.53
	weight: 0.3
	merge_method: dare_ties
	base_model: mistralai/Mistral-7B-v0.1
	parameters:
	int8_mask: true
	dtype: bfloat16