Instructions to use MultivexAI/Everyday-Language-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MultivexAI/Everyday-Language-3B with Transformers:

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

pipe = pipeline("text-generation", model="MultivexAI/Everyday-Language-3B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MultivexAI/Everyday-Language-3B")
model = AutoModelForCausalLM.from_pretrained("MultivexAI/Everyday-Language-3B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MultivexAI/Everyday-Language-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MultivexAI/Everyday-Language-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MultivexAI/Everyday-Language-3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MultivexAI/Everyday-Language-3B

SGLang

How to use MultivexAI/Everyday-Language-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 "MultivexAI/Everyday-Language-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": "MultivexAI/Everyday-Language-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 "MultivexAI/Everyday-Language-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": "MultivexAI/Everyday-Language-3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MultivexAI/Everyday-Language-3B with Docker Model Runner:
```
docker model run hf.co/MultivexAI/Everyday-Language-3B
```

MultivexAI commited on Aug 6, 2025

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@@ -15,10 +15,6 @@ Everyday-Language-3B is a language model fine-tuned for generating natural, ever
 This fine-tuning process significantly improves the model's ability to produce coherent, contextually appropriate, and less repetitive text compared to its base version. It aims to better capture the nuances and patterns of typical conversational language.
-# HellaSwag Benchmark
-**An evaluation using a non-instruction-following method was performed on HellaSwag (1000 validation samples). By calculating the likelihood of each possible continuation, the model scored ~73.4% accuracy. This reflects its performance as a base text predictor on a commonsense task, separate from its intended use for generating everyday conversational text.**
 ## Intended Uses & Limitations
 **Intended Uses:**

 This fine-tuning process significantly improves the model's ability to produce coherent, contextually appropriate, and less repetitive text compared to its base version. It aims to better capture the nuances and patterns of typical conversational language.
 ## Intended Uses & Limitations
 **Intended Uses:**