Laguna XS 2.1
Laguna XS 2.1A 33B total parameter Mixture-of-Experts model with 3B activated parameters per token.
Model details
View repositoryExample usage
Laguna XS 2.1 is a 33B total parameter Mixture-of-Experts model with 3B activated parameters per token designed for agentic coding and long-horizon work, achieving 47.6% on SWE-Bench Pro. 256K context length. This model is an upgraded version of Poolside’s Laguna XS.2 model with stronger performance on terminal-style tasks. Licensed under OpenMDW-1.1, an open, permissive license for models from the Linux Foundation.
This model delivers the strongest performance in Poolside’s agent harness, pool, after undergoing agent RL. Poolside’s RL stack is a custom-built system loosely coupling the major components of inference and rollout generation, orchestration of code execution sandboxes, trajectory scoring, buffering and filtering, and distributed training.
1# You can use this model with any of the OpenAI clients in any language!
2# Simply set the API Key to get started
3
4import os
5from openai import OpenAI
6
7model_url = "" # Copy in from API pane in Baseten model dashboard
8
9client = OpenAI(
10 api_key=os.environ['BASETEN_API_KEY'],
11 base_url=model_url,
12)
13
14response = client.chat.completions.create(
15 model="poolside/laguna-xs-2.1",
16 messages=[
17 {
18 "role": "user",
19 "content": "Implement Hello World in Python",
20 }
21 ],
22 stream=True,
23 stream_options={
24 "include_usage": True,
25 "continuous_usage_stats": True,
26 },
27 top_p=1,
28 max_tokens=1000,
29 temperature=1,
30 presence_penalty=0,
31 frequency_penalty=0,
32)
33
34for chunk in response:
35 if chunk.choices and chunk.choices[0].delta.content is not None:
36 print(chunk.choices[0].delta.content, end="", flush=True)1{
2 "id": "143",
3 "choices": [
4 {
5 "finish_reason": "stop",
6 "index": 0,
7 "logprobs": null,
8 "message": {
9 "content": "[Model output here]",
10 "role": "assistant",
11 "audio": null,
12 "function_call": null,
13 "tool_calls": null
14 }
15 }
16 ],
17 "created": 1741224586,
18 "model": "",
19 "object": "chat.completion",
20 "service_tier": null,
21 "system_fingerprint": null,
22 "usage": {
23 "completion_tokens": 145,
24 "prompt_tokens": 38,
25 "total_tokens": 183,
26 "completion_tokens_details": null,
27 "prompt_tokens_details": null
28 }
29}
