Text Generation
Transformers
Safetensors
ncp_smol
next-concept-prediction
conceptlm
causal-lm
smollm2
tessera
custom_code
Instructions to use yava-code/Tessera-135M-Gate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Download metrics.jsonl from yava-code/Tessera-135M-Gate: direct link, hf CLI and curl.
- Browser
- Download file 136 kB
-
https://huggingface.co/yava-code/Tessera-135M-Gate/resolve/main/metrics.jsonl
- Command line
-
hf download hf://yava-code/Tessera-135M-Gate/metrics.jsonl
-
curl -L -o metrics.jsonl https://huggingface.co/yava-code/Tessera-135M-Gate/resolve/main/metrics.jsonl
136 kB
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| {"estimated_cost_usd": 0.0032966898417222217, "event": "train", "grad_norm": 2.2670326232910156, "learning_rate": 8.048780487804879e-06, "loss": 4.751682639122009, "ncp_loss": 1.3053224384784698, "ntp_loss": 2.135641485452652, "step": 10, "tokens": 40960, "tokens_per_second": 3175.171477486401, "vq_loss": 1.3107188642024994} | |
| {"estimated_cost_usd": 0.0054161960665, "event": "train", "grad_norm": 1.8863328695297241, "learning_rate": 1.5365853658536586e-05, "loss": 4.725244164466858, "ncp_loss": 1.3087759017944336, "ntp_loss": 2.1074967086315155, "step": 20, "tokens": 81920, "tokens_per_second": 3865.2794053372572, "vq_loss": 1.3089716136455536} | |
| {"estimated_cost_usd": 0.007519445111322222, "event": "train", "grad_norm": 1.772733211517334, "learning_rate": 2.2682926829268295e-05, "loss": 4.656316518783569, "ncp_loss": 1.321251004934311, "ntp_loss": 2.009916454553604, "step": 30, "tokens": 122880, "tokens_per_second": 4176.1946794960795, "vq_loss": 1.3251491487026215} | |
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| {"estimated_cost_usd": 0.029731484192633335, "event": "train", "grad_norm": 1.2922022342681885, "learning_rate": 2.996792034129368e-05, "loss": 4.45792019367218, "ncp_loss": 1.3251806199550629, "ntp_loss": 1.804277777671814, "step": 130, "tokens": 532480, "tokens_per_second": 4576.906465232529, "vq_loss": 1.3284618258476257} | |
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| {"estimated_cost_usd": 0.07282531832374443, "event": "train", "grad_norm": 1.244828701019287, "learning_rate": 2.9663021342318226e-05, "loss": 4.320633411407471, "ncp_loss": 1.3036129772663116, "ntp_loss": 1.7122487425804138, "step": 330, "tokens": 1351680, "tokens_per_second": 4743.258818282532, "vq_loss": 1.3047715425491333} | |
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| {"estimated_cost_usd": 0.09400590512719999, "event": "train", "grad_norm": 1.2929503917694092, "learning_rate": 2.939154094822746e-05, "loss": 4.272367358207703, "ncp_loss": 1.2587842643260956, "ntp_loss": 1.7507393062114716, "step": 430, "tokens": 1761280, "tokens_per_second": 4788.049094148384, "vq_loss": 1.2628435492515564} | |
| {"estimated_cost_usd": 0.09613180937058888, "event": "train", "grad_norm": 1.2165906429290771, "learning_rate": 2.936010826492746e-05, "loss": 4.115151643753052, "ncp_loss": 1.2577183544635773, "ntp_loss": 1.5955350697040558, "step": 440, "tokens": 1802240, "tokens_per_second": 4791.051447590402, "vq_loss": 1.2618982195854187} | |
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