Instructions to use Lambent/CosMoEAlpacaLisa-4x1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lambent/CosMoEAlpacaLisa-4x1b with Transformers:

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

pipe = pipeline("text-generation", model="Lambent/CosMoEAlpacaLisa-4x1b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lambent/CosMoEAlpacaLisa-4x1b")
model = AutoModelForCausalLM.from_pretrained("Lambent/CosMoEAlpacaLisa-4x1b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Lambent/CosMoEAlpacaLisa-4x1b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lambent/CosMoEAlpacaLisa-4x1b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lambent/CosMoEAlpacaLisa-4x1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Lambent/CosMoEAlpacaLisa-4x1b

SGLang

How to use Lambent/CosMoEAlpacaLisa-4x1b 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/CosMoEAlpacaLisa-4x1b" \
    --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/CosMoEAlpacaLisa-4x1b",
		"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/CosMoEAlpacaLisa-4x1b" \
        --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/CosMoEAlpacaLisa-4x1b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Lambent/CosMoEAlpacaLisa-4x1b with Docker Model Runner:
```
docker model run hf.co/Lambent/CosMoEAlpacaLisa-4x1b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Intuitively it seemed like LISA training should suit a MoE pretty well; though I don't know how well calibrated my intuitions are.

Interesting thing about this one is it looks like it wasn't converging at the end of one epoch. Still more to learn.

Nous capabilities:

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
CosMoEAlpacaLisa-4x1b	23.44	48.13	41.13	29.95	35.66

Comparisons:

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
CosMoE-AlpacaLight-v0.6	23.3	52.15	38.57	29.01	35.76

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

Model	AGIEval	GPT4All	TruthfulQA	Bigbench	Average
cosmo-1b	22.97	52.01	38.02	28.73	35.43

See axolotl config

axolotl version: 0.4.0

base_model: Lambent/cosmoem-4x1b
model_type: AutoModelForCausalLM
tokenizer_type: LlamaTokenizer
trust_remote_code: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: vicgalle/alpaca-gpt4
    type: alpaca
dataset_prepared_path: prepared-alpaca
val_set_size: 0.05
output_dir: ./lisa-out

sequence_len: 2048
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true

lisa_n_layers: 4
lisa_step_interval: 10
lisa_layers_attribute: model.layers

adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project: CosMoE-AlpacaLisa
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0005

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

loss_watchdog_threshold: 3.0
loss_watchdog_patience: 3

warmup_steps: 20
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.002
fsdp:
fsdp_config:
special_tokens:

lisa-out

This model is a fine-tuned version of Lambent/cosmoem-4x1b on the None dataset. It achieves the following results on the evaluation set:

Loss: 1.2588

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0005
train_batch_size: 1
eval_batch_size: 1
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 20
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.197	0.0	1	1.5990
1.4959	0.25	1383	1.4359
1.6549	0.5	2766	1.3353
1.3571	0.75	4149	1.2588

Framework versions

Transformers 4.40.0.dev0
Pytorch 2.1.2+cu118
Datasets 2.18.0
Tokenizers 0.15.0

Downloads last month: 11

Safetensors

Model size

5B params

Tensor type

F32

BF16

Model tree for Lambent/CosMoEAlpacaLisa-4x1b

Base model

Lambent/cosmoem-4x1b

Finetuned

(2)

this model

Quantizations

1 model