Instructions to use antiven0m/finch-6bpw-exl2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use antiven0m/finch-6bpw-exl2 with Transformers:

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

pipe = pipeline("text-generation", model="antiven0m/finch-6bpw-exl2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("antiven0m/finch-6bpw-exl2")
model = AutoModelForCausalLM.from_pretrained("antiven0m/finch-6bpw-exl2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use antiven0m/finch-6bpw-exl2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "antiven0m/finch-6bpw-exl2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "antiven0m/finch-6bpw-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/antiven0m/finch-6bpw-exl2

SGLang

How to use antiven0m/finch-6bpw-exl2 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 "antiven0m/finch-6bpw-exl2" \
    --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": "antiven0m/finch-6bpw-exl2",
		"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 "antiven0m/finch-6bpw-exl2" \
        --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": "antiven0m/finch-6bpw-exl2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use antiven0m/finch-6bpw-exl2 with Docker Model Runner:
```
docker model run hf.co/antiven0m/finch-6bpw-exl2
```

Finch 7b Merge

A SLERP merge of my two current fav 7B models

macadeliccc/WestLake-7B-v2-laser-truthy-dpo & SanjiWatsuki/Kunoichi-DPO-v2-7B

A 6bpw EXL2 quant of Finch

I'm open to doing others quants, just ask.

GGUF Quants

Settings

I reccomend using the ChatML format. As for samplers, I reccomend the following:

Temperature: 1.2
Min P: 0.2
Smoothing Factor: 0.2

Mergekit Config

base_model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
dtype: float16
merge_method: slerp
parameters:
  t:
  - filter: self_attn
    value: [0.0, 0.5, 0.3, 0.7, 1.0]
  - filter: mlp
    value: [1.0, 0.5, 0.7, 0.3, 0.0]
  - value: 0.5
slices:
- sources:
  - layer_range: [0, 32]
    model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
  - layer_range: [0, 32]
    model: SanjiWatsuki/Kunoichi-DPO-v2-7B

Downloads last month: 11

Model tree for antiven0m/finch-6bpw-exl2

SanjiWatsuki/Kunoichi-DPO-v2-7B

macadeliccc/WestLake-7B-v2-laser-truthy-dpo

Merge model

this model