Instructions to use yava-code/Tessera-135M-Gate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yava-code/Tessera-135M-Gate with Transformers:

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

pipe = pipeline("text-generation", model="yava-code/Tessera-135M-Gate", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("yava-code/Tessera-135M-Gate", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yava-code/Tessera-135M-Gate with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yava-code/Tessera-135M-Gate"
# 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-135M-Gate",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yava-code/Tessera-135M-Gate

SGLang

How to use yava-code/Tessera-135M-Gate 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-135M-Gate" \
    --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-135M-Gate",
		"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-135M-Gate" \
        --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-135M-Gate",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yava-code/Tessera-135M-Gate with Docker Model Runner:
```
docker model run hf.co/yava-code/Tessera-135M-Gate
```

Tessera-135M-Gate

File size: 534 Bytes

7709bf3

{
  "dataset": "roneneldan/TinyStories",
  "dtype": "uint32",
  "eos_token_id": 0,
  "revision": "f54c09fd23315a6f9c86f9dc80f725de7d8f9c64",
  "sequence_length": 256,
  "subset": null,
  "tokenizer": "HuggingFaceTB/SmolLM2-135M",
  "tokenizer_revision": "93efa2f097d58c2a74874c7e644dbc9b0cee75a2",
  "train_sha256": "1b72ac9f0a05d841df30feb6feb8fbd7c38170d61528e78cfea3b04c491cc025",
  "train_tokens": 1048576,
  "validation_sha256": "6f0c6b7c842665a23d593f34c00ac7d7a9a9b3050a3d1d1592fac27b12be2462",
  "validation_tokens": 1048576
}