Instructions to use Locutusque/TinyMistral-248M-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Locutusque/TinyMistral-248M-v2 with Transformers:

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

pipe = pipeline("text-generation", model="Locutusque/TinyMistral-248M-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Locutusque/TinyMistral-248M-v2")
model = AutoModelForCausalLM.from_pretrained("Locutusque/TinyMistral-248M-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Locutusque/TinyMistral-248M-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Locutusque/TinyMistral-248M-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Locutusque/TinyMistral-248M-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Locutusque/TinyMistral-248M-v2

SGLang

How to use Locutusque/TinyMistral-248M-v2 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 "Locutusque/TinyMistral-248M-v2" \
    --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": "Locutusque/TinyMistral-248M-v2",
		"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 "Locutusque/TinyMistral-248M-v2" \
        --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": "Locutusque/TinyMistral-248M-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Locutusque/TinyMistral-248M-v2 with Docker Model Runner:
```
docker model run hf.co/Locutusque/TinyMistral-248M-v2
```

Locutusque commited on Jan 8, 2024

Commit

0f57b17

1 Parent(s): 0d48d48

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -0

README.md CHANGED Viewed

@@ -29,6 +29,8 @@ widget:
     form of cognitive-behavioral therapy whereby addicts learn to reduce their
     reactivity to drug-paired stimuli through cue-exposure or extinction
     training. It is, however,
 ---
 # Training
 This model was trained on two datasets, shown in this model page.

     form of cognitive-behavioral therapy whereby addicts learn to reduce their
     reactivity to drug-paired stimuli through cue-exposure or extinction
     training. It is, however,
+- text: >-
+    \begin{document} \begin{frontmatter} \author{Mahouton Norbert Hounkonnou\corref{cor1}${}^1$} \cortext[cor1]{norbert.hounkonnou@cipma.uac.bj} \author{Sama Arjika\corref{cor2}${}^1$} \cortext[cor2]{rjksama2008@gmail.com} \author{ Won Sang Chung\corref{cor3}${}^2$ } \cortext[cor3]{mimip4444@hanmail.net} \title{\bf New families of $q$ and $(q;p)-$Hermite polynomials } \address{${}^1$International Chair of Mathematical Physics and Applications \\ (ICMPA-UNESCO Chair), University of Abomey-Calavi,\\ 072 B. P.: 50 Cotonou, Republic of Benin,\\ ${}^2$Department of Physics and Research Institute of Natural Science, \\ College of Natural Science, \\ Gyeongsang National University, Jinju 660-701, Korea } \begin{abstract} In this paper, we construct a new family of $q-$Hermite polynomials denoted by $H_n(x,s|q).$ Main properties and relations are established and
 ---
 # Training
 This model was trained on two datasets, shown in this model page.