Instructions to use NLPnorth/snakmodel-7b-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NLPnorth/snakmodel-7b-base with Transformers:

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

pipe = pipeline("text-generation", model="NLPnorth/snakmodel-7b-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NLPnorth/snakmodel-7b-base")
model = AutoModelForCausalLM.from_pretrained("NLPnorth/snakmodel-7b-base")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use NLPnorth/snakmodel-7b-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NLPnorth/snakmodel-7b-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NLPnorth/snakmodel-7b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/NLPnorth/snakmodel-7b-base

SGLang

How to use NLPnorth/snakmodel-7b-base 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 "NLPnorth/snakmodel-7b-base" \
    --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": "NLPnorth/snakmodel-7b-base",
		"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 "NLPnorth/snakmodel-7b-base" \
        --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": "NLPnorth/snakmodel-7b-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use NLPnorth/snakmodel-7b-base with Docker Model Runner:
```
docker model run hf.co/NLPnorth/snakmodel-7b-base
```

personads commited on Oct 18, 2024

Commit

8fcecee

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@@ -5,12 +5,13 @@ license: llama2
 base_model:
 - meta-llama/Llama-2-7b-hf
 pipeline_tag: text-generation
 ---
 ![SnakModel Base Logo](snakmodel-base.png)
 ## Model Details
-**SnakModel** is a 7B-parameter model specifically designed for the Danish language. This is the base variant: `SnakModel-7B (base)`. Our models builds upon [Llama 2](https://huggingface.co/meta-llama/Llama-2-7b-hf), which we continuously pre-train on a diverse collection of Danish corpora comprising 350M documents and 13.6B words, before tuning it on 3.7M Danish instruction-answer pairs.
 **Model Developers**

 base_model:
 - meta-llama/Llama-2-7b-hf
 pipeline_tag: text-generation
+library_name: transformers
 ---
 ![SnakModel Base Logo](snakmodel-base.png)
 ## Model Details
+**SnakModel** is a 7B-parameter model specifically designed for the Danish language. This is the base variant: `SnakModel-7B (base)`. Our models build upon [Llama 2](https://huggingface.co/meta-llama/Llama-2-7b-hf), which we continuously pre-train on a diverse collection of Danish corpora comprising 350M documents and 13.6B words, before tuning it on 3.7M Danish instruction-answer pairs.
 **Model Developers**