Undi95: ReMM SLERP 13B
Open-weight instruction model for adaptable chat and self-hosted production workloads
- Model lab
- Undi95
- Model family
- —
Quickstart
Use your Moduck API Key, set the API base URL to https://api.moduck.ai/v1, and set the model parameter to moduck/undi95/remm-slerp-l2-13b.
import os
import requests
base_url = "https://api.moduck.ai/v1"
api_key = os.environ["MODUCK_API_KEY"]
model_id = "moduck/undi95/remm-slerp-l2-13b"
response = requests.post(
f"{base_url}/chat/completions",
headers={
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
},
json={
"model": model_id,
"messages": [{"role": "user", "content": "Hello!"}],
"stream": False,
},
timeout=60,
)
response.raise_for_status()
print(response.json())Use the familiar OpenAI-style chat request format to make model integration easier.
ReMM SLERP 13B API Pricing
| Type | Price information |
|---|---|
| Input · Context ≤ 6144 tokens · Maximum output 4096 tokens | USD 0.55016 / 1M tokens |
| Output · Context ≤ 6144 tokens · Maximum output 4096 tokens | USD 0.82524 / 1M tokens |
Performance
Measured on the Moduck gateway across live traffic, hour by hour over the last 24 hours. Time to first token is how long before anything appears; throughput is how fast the answer is written after that; end-to-end latency is the whole round trip. Figures are medians unless the card says otherwise.
Performance trends
Availability
Uptime is the share of time at least one provider serving this model responded. Availability is stricter: the share of requests actually served. A provider that answers but fails part of the traffic lowers availability without lowering uptime. Both cover the last three days.
ReMM SLERP 13B Compare models
| Model | Input price | Output price | Context window | Capabilities |
|---|---|---|---|---|
| ReMM SLERP 13BThis page | USD 0.55016 / 1M tokens · Context ≤ 6144 tokens · Maximum output 4096 tokens | USD 0.82524 / 1M tokens · Context ≤ 6144 tokens · Maximum output 4096 tokens | 6,144 | Text output |
| Beam 1.0 501B A23B | USD 0.2 / 1M tokens | USD 0.8 / 1M tokens | 1,048,576 | Text output · Reasoning · Tool calling |
| Ling 3.1 Flash | USD 0.06 / 1M tokens | USD 0.176 / 1M tokens | 262,144 | Text output · Reasoning · Tool calling |
| Schematron V2 Small | USD 0.04 / 1M tokens | USD 0.184 / 1M tokens | 128,000 | Text output |
| Schematron V2 Turbo | USD 0.024 / 1M tokens | USD 0.12 / 1M tokens | 128,000 | Text output |
Model specifications
- Input
- text
- Output
- text
- Context window
- 6,144
- Maximum output
- 5,529
- Release date
- 2023-07-22
- Attachments
- No
- Reasoning
- No
- Tool calling
- No
- Structured output
- Yes
- Temperature setting
- Yes
- Open weights
- Yes
Frequently asked questions
What is ReMM SLERP 13B for?
Open-weight instruction model for adaptable chat and self-hosted production workloads
What inputs and outputs does ReMM SLERP 13B support?
Recorded inputs: text; outputs: text.
How is ReMM SLERP 13B priced?
The pricing table shows the current recorded Moduck price. For example: Input · Context ≤ 6144 tokens · Maximum output 4096 tokens USD 0.55016 / 1M tokens; Output · Context ≤ 6144 tokens · Maximum output 4096 tokens USD 0.82524 / 1M tokens. See the pricing table for all units, variants, and conditions.
What specifications are recorded for ReMM SLERP 13B?
Current model records list Context window 6,144 tokens; Maximum output 5,529 tokens.
What model parameter does the Quickstart use?
The example uses moduck/undi95/remm-slerp-l2-13b. This is a cataloged Moduck public or reserved ID; check your account’s available models before calling it.
Can I call this model through the Moduck API now?
This page has no verified Moduck API call for this model. A Quickstart example or reserved ID does not establish access; check the models available to your account.
Related models
No related catalog models are recorded for this model family.