Instructions to use yam-peleg/Hebrew-Mistral-7B-200K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yam-peleg/Hebrew-Mistral-7B-200K with Transformers:

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

pipe = pipeline("text-generation", model="yam-peleg/Hebrew-Mistral-7B-200K")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yam-peleg/Hebrew-Mistral-7B-200K")
model = AutoModelForCausalLM.from_pretrained("yam-peleg/Hebrew-Mistral-7B-200K")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yam-peleg/Hebrew-Mistral-7B-200K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yam-peleg/Hebrew-Mistral-7B-200K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yam-peleg/Hebrew-Mistral-7B-200K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yam-peleg/Hebrew-Mistral-7B-200K

SGLang

How to use yam-peleg/Hebrew-Mistral-7B-200K 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 "yam-peleg/Hebrew-Mistral-7B-200K" \
    --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": "yam-peleg/Hebrew-Mistral-7B-200K",
		"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 "yam-peleg/Hebrew-Mistral-7B-200K" \
        --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": "yam-peleg/Hebrew-Mistral-7B-200K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yam-peleg/Hebrew-Mistral-7B-200K with Docker Model Runner:
```
docker model run hf.co/yam-peleg/Hebrew-Mistral-7B-200K
```

yam-peleg commited on May 5, 2024

Commit

ba1930b

verified ·

1 Parent(s): 296ba0c

Update tokenizer_config.json

Browse files

Files changed (1) hide show

tokenizer_config.json +1 -1

tokenizer_config.json CHANGED Viewed

@@ -40,7 +40,7 @@
   "clean_up_tokenization_spaces": false,
   "eos_token": "</s>",
   "legacy": true,
-  "model_max_length": 2048,
   "pad_token": "[PAD]",
   "padding_side": "right",
   "sp_model_kwargs": {},

   "clean_up_tokenization_spaces": false,
   "eos_token": "</s>",
   "legacy": true,
+  "model_max_length": 262144,
   "pad_token": "[PAD]",
   "padding_side": "right",
   "sp_model_kwargs": {},