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 / trainer_state.json

yava-code

Publish Tessera-1B-Nano-Base checkpoint

42322ca verified about 1 month ago

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145 Bytes

{
  "batcher": {
    "cursor": 976512,
    "epoch": 0
  },
  "billable_seconds": 32693.896218564998,
  "step": 7629,
  "tokens_seen": 999948288
}

	{
	"batcher": {
	"cursor": 976512,
	"epoch": 0
	},
	"billable_seconds": 32693.896218564998,
	"step": 7629,
	"tokens_seen": 999948288
	}