Instructions to use Lambent/CosmoAlpacaLisa-0.3-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lambent/CosmoAlpacaLisa-0.3-1b with Transformers:

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

pipe = pipeline("text-generation", model="Lambent/CosmoAlpacaLisa-0.3-1b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lambent/CosmoAlpacaLisa-0.3-1b")
model = AutoModelForCausalLM.from_pretrained("Lambent/CosmoAlpacaLisa-0.3-1b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Lambent/CosmoAlpacaLisa-0.3-1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lambent/CosmoAlpacaLisa-0.3-1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lambent/CosmoAlpacaLisa-0.3-1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Lambent/CosmoAlpacaLisa-0.3-1b

SGLang

How to use Lambent/CosmoAlpacaLisa-0.3-1b 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 "Lambent/CosmoAlpacaLisa-0.3-1b" \
    --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": "Lambent/CosmoAlpacaLisa-0.3-1b",
		"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 "Lambent/CosmoAlpacaLisa-0.3-1b" \
        --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": "Lambent/CosmoAlpacaLisa-0.3-1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Lambent/CosmoAlpacaLisa-0.3-1b with Docker Model Runner:
```
docker model run hf.co/Lambent/CosmoAlpacaLisa-0.3-1b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Figured the model must have learned the data a somewhat different way for all three training methods. Decided to test if merging them would give generalization benefits. I think it didn't hurt at least!

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
CosmoAlpacaLisa-0.3-1b	23.79	51.61	40.25	29.97	36.41

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
CosmoAlpacaLight-1b	24.28	51.31	40.33	29.47	36.35

lisacosmo

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 HuggingFaceTB/cosmo-1b as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: Lambent/CosmoAlpacaLisa-1b
    parameters:
      density: 1.0
      weight: 1.0
  - model: Lambent/CosmoAlpacaLisa-0.2-1b
    parameters:
      density: 1.0
      weight: 1.0
  - model: Lambent/CosmoAlpacaLight-1b
    parameters:
      density: 1.0
      weight: 1.0
merge_method: dare_ties
base_model: HuggingFaceTB/cosmo-1b
parameters:
  normalize: true
  int8_mask: false
dtype: float16