# CLM V0.1 8B

URL: https://interfaze.ai/models/contrastive-lmclm-v01-8b

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CLM V0.1 8B by Contrastive-LM, a text-ranking model. Understand and compare features, benchmarks, and capabilities.

## Comparison

| Feature | CLM V0.1 8B | Interfaze |
| --- | --- | --- |
| Input Modalities | text | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | unknown | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | unknown | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | CLM V0.1 8B | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B)

**Contrastive Language Model (CLM)** is a new class of **System One model** trained with a **contrastive learning** objective that connects **states and actions**. **CLM-8B** consists of two small projection heads (a state head and an action head) on top of a frozen **Qwen3-8B** encoder trained with a bidirectional InfoNCE loss.

-   **Training:** pre-trained on ~60M Nemotron Q&A pairs, mid-trained on ~30M synthetic hard negatives, post-trained on ~1M agentic trajectories.
-   **Zero-shot:** on par with Jev on computer-use, gaming and tool-calling tasks, with **up to 9× lower latency**.
-   **Fine-tuned as a verifier:** SOTA on **DeepSWE (81.6%)** and **Terminal-Bench 2.1 (87.6%)**, 4–6× faster than Jev.
-   **State & Action Caching:** states and actions are encoded separately, so action embeddings can be reused. **With ~1k candidates, CLM is 13× faster than Jev.**

## Usage

### With the `contrastive-lm` package

```
pip install contrastive-lm


vllm serve Qwen/Qwen3-8B --served-model-name qwen3-8b --runner pooling --max-model-len 2048 --port 8090 &


clm-serve
```

Ask typed questions about a state:

```
from clm import CLMClient, Choice, Noul, Score

client = CLMClient()  # http://127.0.0.1:8700 by default
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["department"].choice)         # billing
print(r.answers["department"].probabilities)  # {'billing': 0.93878, 'technical': 0.06122}
```

Or rank free-form candidates (best-of-N solutions, tool names, next moves):

```
from clm import Engine

engine = Engine(emb_url="http://127.0.0.1:8090/v1/embeddings")
engine.rank("What causes tides on Earth?",
            ["The Moon's gravitational pull.", "Photosynthesis in plants.", "Because the Earth is round."])
```

### Fine-tuning

Only the heads are trained, so fine-tuning is cheap. This checkpoint is the starting point for the DeepSWE and Terminal-Bench heads.

```
git clone https://github.com/Contrastive-LM/CLM.git && cd CLM && pip install -e .
hf download Contrastive-LM/deepswe-clm-heads-8k heldout_tasks.json --local-dir heads/deepswe
python train/finetune.py --task clm --init-ckpt "$(clm-download)" --out-dir runs/deepswe \
    --holdout-tasks heads/deepswe/heldout_tasks.json --batch 512
```

See the [fine-tuning guide](https://github.com/Contrastive-LM/CLM/blob/main/docs/FINETUNING.md).

### Playground

`clm-serve` also serves a web playground at `http://localhost:8700/`.

## Limitations

-   **Encoder-locked:** the heads require Qwen3-8B last-token-pooled embeddings.
-   **No generation:** CLM only scores the candidates you give it, and its probabilities are relative to that set.
-   **Verifier results need fine-tuning:** the SOTA agentic-benchmark numbers come from fine-tuned heads, not this checkpoint zero-shot.
-   **Generalization:** CLM-8B is one rung of our scaling ladder. A multimodal **CLM-35B**, trained with more data, compute and parameters for stronger generalization, is coming in early October.

## Citation

```
@misc{kwok2026contrastivelanguagemodels,
  title={Contrastive Language Models: A System One Model for Fast and Generalizable Decision-Making},
  author={Jacky Kwok and Hangoo Kang and Tarun Suresh and Jon Saad-Falcon and Marco Pavone and Christopher Ré and Azalia Mirhoseini},
  year={2026},
  note={Notion Blog},
  url={https://contrastive-lm.notion.site}
}
```

## License

The CLM-8B weights are released under the [Apache 2.0 License](https://huggingface.co/Contrastive-LM/CLM-v0.1-8B/blob/main/LICENSE). The base encoder [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) is also Apache 2.0.

## Want more deterministic results?

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)