Instructions to use RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits with Transformers:

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

pipe = pipeline("text-generation", model="RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits")
model = AutoModelForCausalLM.from_pretrained("RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits

SGLang

How to use RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits 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 "RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits" \
    --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": "RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits",
		"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 "RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits" \
        --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": "RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits
```

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

fairseq-dense-2.7B - bnb 8bits

Model creator: https://huggingface.co/KoboldAI/
Original model: https://huggingface.co/KoboldAI/fairseq-dense-2.7B/

Original model description:

language: en

This is a Hugging Face transformers-compatible conversion of the original dense 2.7B-parameter model from the paper "Efficient Large Scale Language Modeling with Mixtures of Experts" from Artetxe et al. Please refer to the original model card, which can be found at https://github.com/facebookresearch/fairseq/blob/main/examples/moe_lm/model_card.md.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	33.67
ARC (25-shot)	33.79
HellaSwag (10-shot)	65.74
MMLU (5-shot)	26.44
TruthfulQA (0-shot)	34.57
Winogrande (5-shot)	63.93
GSM8K (5-shot)	0.0
DROP (3-shot)	11.24

Downloads last month: 4

Safetensors

Model size

3B params

Tensor type

F32

F16

Paper for RichardErkhov/KoboldAI_-_fairseq-dense-2.7B-8bits

Efficient Large Scale Language Modeling with Mixtures of Experts

Paper • 2112.10684 • Published Dec 20, 2021 • 2