Instructions to use Noodlz/DolphinLake-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Noodlz/DolphinLake-7B with Transformers:

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

pipe = pipeline("text-generation", model="Noodlz/DolphinLake-7B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Noodlz/DolphinLake-7B")
model = AutoModelForCausalLM.from_pretrained("Noodlz/DolphinLake-7B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Noodlz/DolphinLake-7B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Noodlz/DolphinLake-7B

SGLang

How to use Noodlz/DolphinLake-7B 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 "Noodlz/DolphinLake-7B" \
    --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": "Noodlz/DolphinLake-7B",
		"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 "Noodlz/DolphinLake-7B" \
        --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": "Noodlz/DolphinLake-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Noodlz/DolphinLake-7B with Docker Model Runner:
```
docker model run hf.co/Noodlz/DolphinLake-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

My first successful Dare-Ties merge. Because of the tokenizer difference of the model types (also bf16 vs f16), Had to use Slerp as well.

Seems to perform well! Did a local lm-eval and HellaSWAG gives me around 84.5, which seems decent. will be submitting this for eval on the openLLM leaderboard as well.

Preset for this should be ChatML, but standard default presets should work ok too.

base_model:

senseable/WestLake-7B-v2
cognitivecomputations/dolphin-2.8-mistral-7b-v02 library_name: transformers tags:
mergekit
merge

Noodlz_DolphinLake-DARE_TIE_SLERP-tokenwest

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 cognitivecomputations/dolphin-2.8-mistral-7b-v02 as a base.

Models Merged

The following models were included in the merge:

senseable/WestLake-7B-v2

Configuration

The following YAML configuration was used to produce this model:

merge_method: dare_ties

parameters:
  int8_mask: true
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5 # fallback for rest of tensors
  embed_slerp: true

models:
  - model: cognitivecomputations/dolphin-2.8-mistral-7b-v02
    # No parameters necessary for base model
  - model: senseable/WestLake-7B-v2
    parameters:
      density: 0.58
      weight: 0.8

base_model: cognitivecomputations/dolphin-2.8-mistral-7b-v02

tokenizer_source: model:senseable/WestLake-7B-v2

dtype: bfloat16