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: 1,467 Bytes

42322ca

{
  "data": {
    "cache_dir": "/vol/.cache/data/fineweb-edu-1b",
    "cache_train_tokens": null,
    "dataset": "HuggingFaceFW/fineweb-edu",
    "overfit": false,
    "revision": "87f09149ef4734204d70ed1d046ddc9ca3f2b8f9",
    "sequence_length": 1024,
    "shuffle_buffer": 10000,
    "subset": "sample-10BT",
    "text_column": "text",
    "train_split": "train",
    "train_tokens": 1000000000,
    "validation_split": null,
    "validation_tokens": 10000000
  },
  "model": {
    "base_model": "HuggingFaceTB/SmolLM2-360M",
    "chunk_size": 4,
    "codebook_size": 64,
    "concept_layers": 2,
    "dtype": "bfloat16",
    "insert_layer": 1,
    "ncp_target": "continuous",
    "ncp_weight": 1.0,
    "revision": "f8027fd0eaeea54caa13c31d31b9fdc459c38b49",
    "segments": 15,
    "vq_weight": 1.0
  },
  "optim": {
    "beta1": 0.9,
    "beta2": 0.95,
    "eps": 1e-08,
    "grad_accum_steps": 16,
    "learning_rate": 3e-05,
    "max_grad_norm": 1.0,
    "micro_batch_size": 8,
    "min_lr_ratio": 0.1,
    "warmup_ratio": 0.01,
    "weight_decay": 0.1
  },
  "run": {
    "mode": "ntp",
    "name": "fineweb-edu-ntp",
    "output_dir": "/vol/runs/fineweb-edu-ntp",
    "resume": true,
    "seed": 17
  },
  "train": {
    "compile": false,
    "eval_batches": 32,
    "eval_every_tokens": 100000000,
    "hourly_cost_usd": 2.74,
    "log_every_steps": 10,
    "max_wall_time_minutes": 1320,
    "run_budget_usd": 60.0,
    "save_every_tokens": 100000000
  }
}