Instructions to use appvoid/no-prompt-1.3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use appvoid/no-prompt-1.3b with Transformers:

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

pipe = pipeline("text-generation", model="appvoid/no-prompt-1.3b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("appvoid/no-prompt-1.3b")
model = AutoModelForCausalLM.from_pretrained("appvoid/no-prompt-1.3b", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use appvoid/no-prompt-1.3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "appvoid/no-prompt-1.3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "appvoid/no-prompt-1.3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/appvoid/no-prompt-1.3b

SGLang

How to use appvoid/no-prompt-1.3b 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 "appvoid/no-prompt-1.3b" \
    --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": "appvoid/no-prompt-1.3b",
		"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 "appvoid/no-prompt-1.3b" \
        --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": "appvoid/no-prompt-1.3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use appvoid/no-prompt-1.3b with Docker Model Runner:
```
docker model run hf.co/appvoid/no-prompt-1.3b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

no-prompt

a sheared-llama-1.3b fine-tuning

This model uses an 1.3 billion parameters model as base to be further fine-tuned on the same data as palmer. It works pretty good and even surpasses sota model on hellaswag.

evaluation

Model	ARC_C	HellaSwag	PIQA	Winogrande
tinyllama-2t	0.2807	0.5463	0.7067	0.5683
palmer-001	0.2807	0.5524	0.7106	0.5896
sheared-1.3b	0.2910	0.5935	0.7339	0.5809
no-prompt-1.3b	0.3157	0.6022	0.7334	0.5864
falcon-rw-1b-instruct-openorca (sota)	0.3362	0.5997	0.7394	0.6148

This model was trained on less than 25% of the dataset yet achieves competitive performance to current sota on open llm leaderboard.

training

Training took ~5 P100 gpu hours. It was trained on 15,000 gpt-4 shuffled samples. no-prompt was fine-tuned using lower learning rates ensuring it keeps as much general knowledge as possible.

prompt

no prompt

limitations

Hallucinations are frequent, just as any transformer model this size.

Downloads last month: 5

Model tree for appvoid/no-prompt-1.3b

Quantizations

1 model

appvoid
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no-prompt-1.3b