Instructions to use yava-code/Tessera-1B-Nano-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yava-code/Tessera-1B-Nano-Base with Transformers:

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

pipe = pipeline("text-generation", model="yava-code/Tessera-1B-Nano-Base")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yava-code/Tessera-1B-Nano-Base")
model = AutoModelForCausalLM.from_pretrained("yava-code/Tessera-1B-Nano-Base", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yava-code/Tessera-1B-Nano-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yava-code/Tessera-1B-Nano-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yava-code/Tessera-1B-Nano-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yava-code/Tessera-1B-Nano-Base

SGLang

How to use yava-code/Tessera-1B-Nano-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 "yava-code/Tessera-1B-Nano-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": "yava-code/Tessera-1B-Nano-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 "yava-code/Tessera-1B-Nano-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": "yava-code/Tessera-1B-Nano-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yava-code/Tessera-1B-Nano-Base with Docker Model Runner:
```
docker model run hf.co/yava-code/Tessera-1B-Nano-Base
```

Tessera-1B-Nano-Base

File size: 551 Bytes

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{
  "dataset": "HuggingFaceFW/fineweb-edu",
  "dtype": "uint32",
  "eos_token_id": 0,
  "revision": "87f09149ef4734204d70ed1d046ddc9ca3f2b8f9",
  "sequence_length": 1024,
  "subset": "sample-10BT",
  "tokenizer": "HuggingFaceTB/SmolLM2-360M",
  "tokenizer_revision": "f8027fd0eaeea54caa13c31d31b9fdc459c38b49",
  "train_sha256": "c42f4bffe9e225eca05ee5fafa9bfc42875d014125f18f47919be611f775a413",
  "train_tokens": 1000000000,
  "validation_sha256": "61ecc77c5d586daa10c5938a6719463380e8e64347c0194e09635651561d4330",
  "validation_tokens": 10000000
}