Instructions to use KoboldAI/fairseq-dense-1.3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KoboldAI/fairseq-dense-1.3B with Transformers:

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

pipe = pipeline("text-generation", model="KoboldAI/fairseq-dense-1.3B")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KoboldAI/fairseq-dense-1.3B")
model = AutoModelForCausalLM.from_pretrained("KoboldAI/fairseq-dense-1.3B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use KoboldAI/fairseq-dense-1.3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "KoboldAI/fairseq-dense-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": "KoboldAI/fairseq-dense-1.3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/KoboldAI/fairseq-dense-1.3B

SGLang

How to use KoboldAI/fairseq-dense-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 "KoboldAI/fairseq-dense-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": "KoboldAI/fairseq-dense-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 "KoboldAI/fairseq-dense-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": "KoboldAI/fairseq-dense-1.3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use KoboldAI/fairseq-dense-1.3B with Docker Model Runner:
```
docker model run hf.co/KoboldAI/fairseq-dense-1.3B
```

Henk717

leaderboard-pr-bot commited on Nov 18, 2023

Commit

3f976ae

1 Parent(s): 20bf173

Adding Evaluation Results (#2)

Browse files

- Adding Evaluation Results (13b3d4509e3b5d11eb18806875db52c07e3625a4)

Co-authored-by: Open LLM Leaderboard PR Bot <leaderboard-pr-bot@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +14 -0

README.md CHANGED Viewed

@@ -2,3 +2,17 @@
 language: en
 ---
 This is a Hugging Face transformers-compatible conversion of the original dense 1.3B-parameter model from the paper "[Efficient Large Scale Language Modeling with Mixtures of Experts](https://arxiv.org/abs/2112.10684)" 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.

 language: en
 ---
 This is a Hugging Face transformers-compatible conversion of the original dense 1.3B-parameter model from the paper "[Efficient Large Scale Language Modeling with Mixtures of Experts](https://arxiv.org/abs/2112.10684)" 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](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_KoboldAI__fairseq-dense-1.3B)
+| Metric                | Value                     |
+|-----------------------|---------------------------|
+| Avg.                  | 31.66   |
+| ARC (25-shot)         | 31.14          |
+| HellaSwag (10-shot)   | 58.39    |
+| MMLU (5-shot)         | 24.98         |
+| TruthfulQA (0-shot)   | 37.43   |
+| Winogrande (5-shot)   | 59.04   |
+| GSM8K (5-shot)        | 0.0        |
+| DROP (3-shot)         | 10.6         |