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 experiment.json from yava-code/Tessera-135M-Gate: direct link, hf CLI and curl.
- Browser
- Download file 1.49 kB
-
https://huggingface.co/yava-code/Tessera-135M-Gate/resolve/main/experiment.json
- Command line
-
hf download hf://yava-code/Tessera-135M-Gate/experiment.json
-
curl -L -o experiment.json https://huggingface.co/yava-code/Tessera-135M-Gate/resolve/main/experiment.json
1.49 kB
| { | |
| "data": { | |
| "cache_dir": "/vol/.cache/data/tinystories-overfit", | |
| "cache_train_tokens": 1048576, | |
| "dataset": "roneneldan/TinyStories", | |
| "overfit": true, | |
| "revision": "f54c09fd23315a6f9c86f9dc80f725de7d8f9c64", | |
| "sequence_length": 256, | |
| "shuffle_buffer": 2000, | |
| "subset": null, | |
| "text_column": "text", | |
| "train_split": "train", | |
| "train_tokens": 16777216, | |
| "validation_split": "validation", | |
| "validation_tokens": 1048576 | |
| }, | |
| "model": { | |
| "base_model": "HuggingFaceTB/SmolLM2-135M", | |
| "chunk_size": 4, | |
| "codebook_size": 64, | |
| "concept_layers": 2, | |
| "dtype": "bfloat16", | |
| "insert_layer": 1, | |
| "ncp_target": "continuous", | |
| "ncp_weight": 1.0, | |
| "revision": "93efa2f097d58c2a74874c7e644dbc9b0cee75a2", | |
| "segments": 9, | |
| "vq_weight": 1.0 | |
| }, | |
| "optim": { | |
| "beta1": 0.9, | |
| "beta2": 0.95, | |
| "eps": 1e-08, | |
| "grad_accum_steps": 4, | |
| "learning_rate": 3e-05, | |
| "max_grad_norm": 1.0, | |
| "micro_batch_size": 4, | |
| "min_lr_ratio": 0.1, | |
| "warmup_ratio": 0.01, | |
| "weight_decay": 0.1 | |
| }, | |
| "run": { | |
| "mode": "ncp", | |
| "name": "tinystories-overfit", | |
| "output_dir": "/vol/runs/tinystories-overfit", | |
| "resume": true, | |
| "seed": 17 | |
| }, | |
| "train": { | |
| "compile": false, | |
| "cost_overhead": 1.15, | |
| "eval_batches": 16, | |
| "eval_every_tokens": 4194304, | |
| "gpu_hourly_usd": 0.8, | |
| "log_every_steps": 10, | |
| "max_wall_time_minutes": 180, | |
| "run_budget_usd": 6.0, | |
| "save_every_tokens": 8388608 | |
| } | |
| } |