Instructions to use ChaoticNeutrals/Pasta-Lake-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChaoticNeutrals/Pasta-Lake-7b with Transformers:

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

pipe = pipeline("text-generation", model="ChaoticNeutrals/Pasta-Lake-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ChaoticNeutrals/Pasta-Lake-7b")
model = AutoModelForCausalLM.from_pretrained("ChaoticNeutrals/Pasta-Lake-7b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ChaoticNeutrals/Pasta-Lake-7b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ChaoticNeutrals/Pasta-Lake-7b

SGLang

How to use ChaoticNeutrals/Pasta-Lake-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 "ChaoticNeutrals/Pasta-Lake-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": "ChaoticNeutrals/Pasta-Lake-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 "ChaoticNeutrals/Pasta-Lake-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": "ChaoticNeutrals/Pasta-Lake-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

Thanks to @Kooten the man the myth the legend we have exl2 quants: https://huggingface.co/models?search=Kooten/Pasta-Lake-7b-exl2

Thanks to @bartowski the homie for the additional exl2 quants, please show him some support aswell: https://huggingface.co/bartowski/Pasta-Lake-7b-exl2/tree/main

Thanks also to @konz00 for the gguf quants: https://huggingface.co/konz00/Pasta-Lake-7b-GGUF

Thanks to @Lewdiculus for the other GGUF quants: https://huggingface.co/Lewdiculous/Pasta-Lake-7b-GGUF

added ST preset files

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: Test157t/Pasta-PrimaMaid-7b
        layer_range: [0, 32]
      - model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
        layer_range: [0, 32]
merge_method: slerp
base_model: Test157t/Pasta-PrimaMaid-7b
parameters:
  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
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	73.07
AI2 Reasoning Challenge (25-Shot)	70.82
HellaSwag (10-Shot)	87.91
MMLU (5-Shot)	64.41
TruthfulQA (0-shot)	68.28
Winogrande (5-shot)	82.64
GSM8k (5-shot)	64.37