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
```

yava-code commited on Oct 1

Commit

4c542e3

verified ·

1 Parent(s): 160b20e

Rewrite card intro: lead with the story, move numbers to a section

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README.md +19 -18

README.md CHANGED Viewed

@@ -13,28 +13,29 @@ license: apache-2.0
 # Tessera-135M-Gate
-The 135M architecture-gate checkpoint from Paragon Intelligence Labs: the smallest
-member of the Tessera family (`HuggingFaceTB/SmolLM2-135M` backbone with the full
-Next Concept Prediction path). It is a deliberately overfit gate run that proves
-the complete concept path trains end to end before any large spend, and carries the
-same causal concept-path architecture as Tessera-1B-Nano. It is not a language
-model quality result: it saw a 1M-token TinyStories subset repeated 16 times.
 ## TL;DR
-This gate checkpoint exists to de-risk the 1B-token comparison, and the scale
-story it records is the point of publishing it.
-- **All three objectives train** (NTP, NCP, VQ fell 41 to 43% over the run).
-- **The decoder causally uses the concept channel even at 135M**: zeroing the
   feedback costs +0.0205 nats of held-out NTP loss.
-- **The failure mode this gate exposed is real and instructive**: effective
-  codebook perplexity stayed near 2.4 (usage ~31-41%), and shuffled feedback cost
-  nothing (+0.000). At 1B tokens the same architecture left the shortcut far
-  behind (perplexity 7.55, usage 85.6%, zero-delta +0.105). Gate small, then
-  scale: some behaviors only appear above a scale threshold.
-## Evaluation
 | Metric | Value |
 | --- | ---: |
@@ -74,8 +75,8 @@ intervention evaluation JSON (also shipped in this repository as `eval.json` and
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-tokenizer = AutoTokenizer.from_pretrained("REPO_ID")
-model = AutoModelForCausalLM.from_pretrained("REPO_ID", trust_remote_code=True)
 ```
 ## The Tessera family

 # Tessera-135M-Gate
+The smallest member of the Tessera family, and the reason the bigger ones exist.
+This 135M `architecture-gate checkpoint` (a `HuggingFaceTB/SmolLM2-135M` backbone
+with the full Next Concept Prediction path) was trained on a deliberately overfit
+TinyStories subset before any large spend, to prove the whole path - concept
+pooling, quantized codes, causal concept blocks, feedback - trains end to end.
+It is not a language-model quality result, and it is not meant to be: what makes
+it worth publishing is what broke, and what the break predicted at scale.
 ## TL;DR
+Small and cheap on purpose: this gate run exists to de-risk the 1B-token comparison before spending on it. It earned its keep in both directions.
+- **All three objectives train** (NTP, NCP, VQ fell 41 to 43% over the run),
+  and the decoder causally uses the concept channel even at 135M: zeroing the
   feedback costs +0.0205 nats of held-out NTP loss.
+- **It also caught a real failure mode**: effective codebook perplexity stayed
+  near 2.4 with usage around a third of the codebook, and shuffled feedback  cost nothing - a low-entropy shortcut the 1B-token run later left far behind.  Gate small, then scale: some behaviors only appear above a scale threshold.
+## Numbers
+The raw record this narrative is built from; per-arm READMEs and the whitepaper hold the
+full analysis.
 | Metric | Value |
 | --- | ---: |
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("yava-code/Tessera-135M-Gate")
+model = AutoModelForCausalLM.from_pretrained("yava-code/Tessera-135M-Gate", trust_remote_code=True)
 ```
 ## The Tessera family