Instructions to use Xenova/tiny-random-MistralForCausalLM_external-data with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Xenova/tiny-random-MistralForCausalLM_external-data with Transformers:

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

pipe = pipeline("text-generation", model="Xenova/tiny-random-MistralForCausalLM_external-data")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Xenova/tiny-random-MistralForCausalLM_external-data")
model = AutoModelForCausalLM.from_pretrained("Xenova/tiny-random-MistralForCausalLM_external-data")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Xenova/tiny-random-MistralForCausalLM_external-data with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Xenova/tiny-random-MistralForCausalLM_external-data

SGLang

How to use Xenova/tiny-random-MistralForCausalLM_external-data 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 "Xenova/tiny-random-MistralForCausalLM_external-data" \
    --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": "Xenova/tiny-random-MistralForCausalLM_external-data",
		"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 "Xenova/tiny-random-MistralForCausalLM_external-data" \
        --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": "Xenova/tiny-random-MistralForCausalLM_external-data",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Xenova/tiny-random-MistralForCausalLM_external-data with Docker Model Runner:
```
docker model run hf.co/Xenova/tiny-random-MistralForCausalLM_external-data
```

Xenova HF Staff commited on Oct 3, 2024

Commit

3bdf6a5

verified ·

1 Parent(s): 692e713

Update config.json

Browse files

Files changed (1) hide show

config.json +3 -0

config.json CHANGED Viewed

@@ -24,6 +24,9 @@
   "sliding_window": 4096,
   "tie_word_embeddings": false,
   "transformers_version": "4.41.0.dev0",
   "type_vocab_size": 16,
   "use_cache": true,
   "vocab_size": 32000

   "sliding_window": 4096,
   "tie_word_embeddings": false,
   "transformers_version": "4.41.0.dev0",
+  "transformers.js_config": {
+    "use_external_data_format": true
+  },
   "type_vocab_size": 16,
   "use_cache": true,
   "vocab_size": 32000