Instructions to use macadeliccc/Laser-WestLake-2x7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use macadeliccc/Laser-WestLake-2x7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="macadeliccc/Laser-WestLake-2x7b")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("macadeliccc/Laser-WestLake-2x7b") model = AutoModelForMultimodalLM.from_pretrained("macadeliccc/Laser-WestLake-2x7b") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use macadeliccc/Laser-WestLake-2x7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "macadeliccc/Laser-WestLake-2x7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "macadeliccc/Laser-WestLake-2x7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/macadeliccc/Laser-WestLake-2x7b
- SGLang
How to use macadeliccc/Laser-WestLake-2x7b 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 "macadeliccc/Laser-WestLake-2x7b" \ --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": "macadeliccc/Laser-WestLake-2x7b", "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 "macadeliccc/Laser-WestLake-2x7b" \ --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": "macadeliccc/Laser-WestLake-2x7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use macadeliccc/Laser-WestLake-2x7b with Docker Model Runner:
docker model run hf.co/macadeliccc/Laser-WestLake-2x7b
metadata
license: apache-2.0
model-index:
- name: Laser-WestLake-2x7b
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 72.27
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/Laser-WestLake-2x7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 88.44
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/Laser-WestLake-2x7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.71
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/Laser-WestLake-2x7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 69.25
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/Laser-WestLake-2x7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 85.79
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/Laser-WestLake-2x7b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.53
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=macadeliccc/Laser-WestLake-2x7b
name: Open LLM Leaderboard
Laser-Westlake-2x7B
This model is a moerge of cognitivecomputations/WestLake-7B-v2-laser and SanjiWatsuki/Kunoichi-DPO-v2-7B
Process
- WestLake-7B-v2-laser is the base model and one of the experts rather than utilizing 3 different models.
- Will attempt to laser the final product as well, but given that the base has already been lasered it may not work out.
- I have another version using the original, non-lasered WestLake-7B that I will also attempt to laser and report the differences.
Usage
Usage is the same as the original WestLake-7B
GGUF
Available here
Evaluations
----Benchmark Complete---- 2024-01-27 19:12:49 Time taken: 24.3 mins Prompt Format: ChatML Model: macadeliccc/Laser-WestLake-2x7b-GGUF Score (v2): 75.42 Parseable: 171.0 --------------- Batch completed Time taken: 24.4 mins ---------------
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 74.00 |
| AI2 Reasoning Challenge (25-Shot) | 72.27 |
| HellaSwag (10-Shot) | 88.44 |
| MMLU (5-Shot) | 64.71 |
| TruthfulQA (0-shot) | 69.25 |
| Winogrande (5-shot) | 85.79 |
| GSM8k (5-shot) | 63.53 |
