README

Contrastive Language Models



A System One Model for Fast and Generalizable Decision-Making



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🔥 Contrastive Language Models (CLMs) are a new class of System One
model trained with a contrastive learning objective that connects
states and actions. This repo serves CLM-8B behind a
TypeSafe-compatible API.

- CLM-8B is pre-trained on 60M Nemotron Q&A pairs, mid-trained on
  30M synthetic hard negatives, and post-trained on 1M agentic
  trajectories.
- It performs on par with Jev across computer-use, gaming and tool-calling
  tasks with up to 9× lower latency. With lightweight fine-tuning it sets a
  new SOTA as a verifier on agentic coding benchmarks: Terminal-Bench 2.1
  (87.6%) and DeepSWE (81.6%).
- States and actions are disaggregated, so their embeddings are cached and
  reused independently, which makes training and serving cheap and blazing fast!

We invite the community to plug it into their own agents and benchmarks!

---

 Installation

bash
pip install contrastive-lm


To install the latest from a clone:

bash
pip install -e .


---

 Quickstart

 Serve

bash
 1. encoder (Qwen3-8B embeddings)
vllm serve Qwen/Qwen3-8B --served-model-name qwen3-8b --runner pooling --max-model-len 2048 --port 8090 &

 2. CLM API on :8700 (downloads the 75 MB reference head on first run)
clm-serve


States longer than 2048 tokens are truncated. For longer states, raise both limits
together, e.g. --max-model-len 8192 on vllm serve and clm-serve --max-tokens 8192
(needs more GPU memory).

 Ask typed questions about a state

python
from clm import CLMClient, Choice, Noul, Score

client = CLMClient()                           CLM_BASE_URL (default http://127.0.0.1:8700), CLM_API_KEY
r = client.system_one(
    state="Customer: my invoice was charged twice and nobody answers the phone!",
    questions={
        "urgency": Noul(instructions="Is this urgent?"),
        "department": Choice(instructions="Which team should handle this?",
                             criteria={"billing": "Charges, invoices, refunds",
                                       "technical": "Bugs and outages"}),
        "frustration": Score(instructions="How frustrated is the customer?",
                             criteria=["Calm", "Frustrated", "Very angry"]),
    },
)
print(r.answers["urgency"].noul)                 0.41022     probability the statement is true
print(r.answers["department"].choice)            billing
print(r.answers["department"].probabilities)     {'billing': 0.93878, 'technical': 0.06122}
print(r.answers["frustration"].score)            1.98386     expected level, 0..2
print(r.usage.input_tokens, r.latency_ms)        38 58.1     (106 tokens on a cold cache: option texts are embedded once)


Questions may be Noul / Choice / Score objects or plain wire-format
dicts, so a request written for TypeSafe replays as
client.system_one(state, questions).

 Rank candidates directly

syste