Prediction

ydideh810/cosmo-speak:fd8ed7161c14133273cfa8fdc60a10143a0107a7d4adeb495ba98f22257d965e

Model

ydideh810/cosmo-speak:fd8ed716

ID

d5en6kq75drgg0ch0n1a584bww

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

2m 40s

Created

12 months ago

Input

debug
top_k: 50
top_p: 0.9
prompt: How do Sun and Earth sensors boost spacecraft navigation and mission success?
temperature: 0.75
max_new_tokens: 128
min_new_tokens: -1

{
  "debug": false,
  "top_k": 50,
  "top_p": 0.9,
  "prompt": "How do Sun and Earth sensors boost spacecraft navigation and mission success?",
  "temperature": 0.75,
  "max_new_tokens": 128,
  "min_new_tokens": -1
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run ydideh810/cosmo-speak using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ydideh810/cosmo-speak:fd8ed7161c14133273cfa8fdc60a10143a0107a7d4adeb495ba98f22257d965e",
  {
    input: {
      debug: false,
      top_k: 50,
      top_p: 0.9,
      prompt: "How do Sun and Earth sensors boost spacecraft navigation and mission success?",
      temperature: 0.75,
      max_new_tokens: 128,
      min_new_tokens: -1
    }
  }
);

console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run ydideh810/cosmo-speak using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ydideh810/cosmo-speak:fd8ed7161c14133273cfa8fdc60a10143a0107a7d4adeb495ba98f22257d965e",
    input={
        "debug": False,
        "top_k": 50,
        "top_p": 0.9,
        "prompt": "How do Sun and Earth sensors boost spacecraft navigation and mission success?",
        "temperature": 0.75,
        "max_new_tokens": 128,
        "min_new_tokens": -1
    }
)

# The ydideh810/cosmo-speak model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/ydideh810/cosmo-speak/api#output-schema
    print(item, end="")

To learn more, take a look at the guide on getting started with Python.

Run ydideh810/cosmo-speak using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "ydideh810/cosmo-speak:fd8ed7161c14133273cfa8fdc60a10143a0107a7d4adeb495ba98f22257d965e",
    "input": {
      "debug": false,
      "top_k": 50,
      "top_p": 0.9,
      "prompt": "How do Sun and Earth sensors boost spacecraft navigation and mission success?",
      "temperature": 0.75,
      "max_new_tokens": 128,
      "min_new_tokens": -1
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

Sun and Earth sensors play an important role in ensuring the accuracy of navigation systems and the success of spacecraft missions. Here’s how: Accurate Navigation: Sun sensors are used to accurately determine the orientation of a spacecraft with respect to the sun. This information is critical for navigating through space, as it helps keep a spacecraft on course and avoid collisions with other objects in orbit. Sun sensors can also be used to determine the position of a spacecraft relative to its surroundings, which is essential for mission planning and execution. Asteroid Probe Mission

{
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  "created_at": "2024-07-30T20:09:16.331000Z",
  "data_removed": false,
  "error": null,
  "id": "d5en6kq75drgg0ch0n1a584bww",
  "input": {
    "debug": false,
    "top_k": 50,
    "top_p": 0.9,
    "prompt": "How do Sun and Earth sensors boost spacecraft navigation and mission success?",
    "temperature": 0.75,
    "max_new_tokens": 128,
    "min_new_tokens": -1
  },
  "logs": "Your formatted prompt is:\nHow do Sun and Earth sensors boost spacecraft navigation and mission success?\nprevious weights were different, switching to https://replicate.delivery/pbxt/rSIuuP8T2eUOJarWa0ZDrY3orSuayx7xBIJjIUmqll9nJxmJA/training_output.zip\nDownloading peft weights\nusing https://replicate.delivery/pbxt/rSIuuP8T2eUOJarWa0ZDrY3orSuayx7xBIJjIUmqll9nJxmJA/training_output.zip instead of https://replicate.delivery/pbxt/rSIuuP8T2eUOJarWa0ZDrY3orSuayx7xBIJjIUmqll9nJxmJA/training_output.zip\nDownloaded training_output.zip as 10 824 kB chunks in 0.3842 with 0 retries\nDownloaded peft weights in 0.384\nUnzipped peft weights in 0.017\nInitialized peft model in 0.008\nOverall initialize_peft took 9.953\nExllama: False\nINFO 07-30 20:11:53 async_llm_engine.py:371] Received request 0: prompt: 'How do Sun and Earth sensors boost spacecraft navigation and mission success?', sampling params: SamplingParams(n=1, best_of=1, presence_penalty=0.0, frequency_penalty=1.0, temperature=0.75, top_p=0.9, top_k=50, use_beam_search=False, length_penalty=1.0, early_stopping=False, stop=['</s>'], ignore_eos=False, max_tokens=128, logprobs=None, skip_special_tokens=True), prompt token ids: None.\nINFO 07-30 20:11:53 llm_engine.py:631] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%\nINFO 07-30 20:11:56 async_llm_engine.py:111] Finished request 0.\nhostname: model-hp-73001d654114dad81ec65da3b834e2f6-888dc6d68-m2gtq",
  "metrics": {
    "predict_time": 13.046766136,
    "total_time": 160.362204
  },
  "output": [
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  ],
  "started_at": "2024-07-30T20:11:43.646438Z",
  "status": "succeeded",
  "urls": {
    "stream": "https://streaming-api.svc.us.c.replicate.net/v1/streams/b4fko4z7r3tfa5euybmqkaav4dwjow24mjrybel6gccnujung2tq",
    "get": "https://api.replicate.com/v1/predictions/d5en6kq75drgg0ch0n1a584bww",
    "cancel": "https://api.replicate.com/v1/predictions/d5en6kq75drgg0ch0n1a584bww/cancel"
  },
  "version": "fd8ed7161c14133273cfa8fdc60a10143a0107a7d4adeb495ba98f22257d965e"
}

Generated in

13.1 seconds

Tweak it Share Report

Prediction

ydideh810/cosmo-speak:fd8ed7161c14133273cfa8fdc60a10143a0107a7d4adeb495ba98f22257d965e

Model

ydideh810/cosmo-speak:fd8ed716

ID

6j6cn4jyy9rgp0ch0nb9g3n3z8

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

3.4s

Created

12 months ago

Input

debug
top_k: 50
top_p: 0.9
prompt: How does payload design differ for manned vs unmanned missions
temperature: 0.75
max_new_tokens: 128
min_new_tokens: -1

{
  "debug": false,
  "top_k": 50,
  "top_p": 0.9,
  "prompt": "How does payload design differ for manned vs unmanned missions",
  "temperature": 0.75,
  "max_new_tokens": 128,
  "min_new_tokens": -1
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run ydideh810/cosmo-speak using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ydideh810/cosmo-speak:fd8ed7161c14133273cfa8fdc60a10143a0107a7d4adeb495ba98f22257d965e",
  {
    input: {
      debug: false,
      top_k: 50,
      top_p: 0.9,
      prompt: "How does payload design differ for manned vs unmanned missions",
      temperature: 0.75,
      max_new_tokens: 128,
      min_new_tokens: -1
    }
  }
);

console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run ydideh810/cosmo-speak using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ydideh810/cosmo-speak:fd8ed7161c14133273cfa8fdc60a10143a0107a7d4adeb495ba98f22257d965e",
    input={
        "debug": False,
        "top_k": 50,
        "top_p": 0.9,
        "prompt": "How does payload design differ for manned vs unmanned missions",
        "temperature": 0.75,
        "max_new_tokens": 128,
        "min_new_tokens": -1
    }
)

# The ydideh810/cosmo-speak model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/ydideh810/cosmo-speak/api#output-schema
    print(item, end="")

To learn more, take a look at the guide on getting started with Python.

Run ydideh810/cosmo-speak using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "ydideh810/cosmo-speak:fd8ed7161c14133273cfa8fdc60a10143a0107a7d4adeb495ba98f22257d965e",
    "input": {
      "debug": false,
      "top_k": 50,
      "top_p": 0.9,
      "prompt": "How does payload design differ for manned vs unmanned missions",
      "temperature": 0.75,
      "max_new_tokens": 128,
      "min_new_tokens": -1
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

? What is the payload design process for manned and unmanned missions different? Does it vary a lot? The two biggest differences are that (1) manned missions are more concerned with safety than performance, and (2) a manned mission is required to carry extra fuel to support the additional weight of the crew. The biggest thing about spacecraft design in general is that they're designed around what's known as an 'as-designed mass'. This means that, when you start out with your design, you make sure that everything in your design has been designed to meet or exceed all

{
  "completed_at": "2024-07-30T20:30:35.595232Z",
  "created_at": "2024-07-30T20:30:32.178000Z",
  "data_removed": false,
  "error": null,
  "id": "6j6cn4jyy9rgp0ch0nb9g3n3z8",
  "input": {
    "debug": false,
    "top_k": 50,
    "top_p": 0.9,
    "prompt": "How does payload design differ for manned vs unmanned missions",
    "temperature": 0.75,
    "max_new_tokens": 128,
    "min_new_tokens": -1
  },
  "logs": "Your formatted prompt is:\nHow does payload design differ for manned vs unmanned missions\ncorrect lora is already loaded\nOverall initialize_peft took 0.000\nExllama: False\nINFO 07-30 20:30:32 async_llm_engine.py:371] Received request 0: prompt: 'How does payload design differ for manned vs unmanned missions', sampling params: SamplingParams(n=1, best_of=1, presence_penalty=0.0, frequency_penalty=1.0, temperature=0.75, top_p=0.9, top_k=50, use_beam_search=False, length_penalty=1.0, early_stopping=False, stop=['</s>'], ignore_eos=False, max_tokens=128, logprobs=None, skip_special_tokens=True), prompt token ids: None.\nINFO 07-30 20:30:32 llm_engine.py:631] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%\nINFO 07-30 20:30:35 async_llm_engine.py:111] Finished request 0.\nhostname: model-hp-73001d654114dad81ec65da3b834e2f6-888dc6d68-vnssc",
  "metrics": {
    "predict_time": 3.065527407,
    "total_time": 3.417232
  },
  "output": [
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    "get": "https://api.replicate.com/v1/predictions/6j6cn4jyy9rgp0ch0nb9g3n3z8",
    "cancel": "https://api.replicate.com/v1/predictions/6j6cn4jyy9rgp0ch0nb9g3n3z8/cancel"
  },
  "version": "fd8ed7161c14133273cfa8fdc60a10143a0107a7d4adeb495ba98f22257d965e"
}

Generated in

3.1 seconds

Tweak it Share Report

Prediction

ydideh810/cosmo-speak:fd8ed7161c14133273cfa8fdc60a10143a0107a7d4adeb495ba98f22257d965e

Model

ydideh810/cosmo-speak:fd8ed716

ID

z997y3zpnnrgg0ch0nba33e128

Status

Succeeded

Source

Web

Hardware

A40 (Large)

Total duration

5.5s

Created

12 months ago

Input

debug
top_k: 50
top_p: 0.9
prompt: How do those sun catcher plates make electricity
temperature: 0.75
max_new_tokens: 128
min_new_tokens: -1

{
  "debug": false,
  "top_k": 50,
  "top_p": 0.9,
  "prompt": "How do those sun catcher plates make electricity",
  "temperature": 0.75,
  "max_new_tokens": 128,
  "min_new_tokens": -1
}

Install Replicate’s Node.js client library:

npm install replicate

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run ydideh810/cosmo-speak using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "ydideh810/cosmo-speak:fd8ed7161c14133273cfa8fdc60a10143a0107a7d4adeb495ba98f22257d965e",
  {
    input: {
      debug: false,
      top_k: 50,
      top_p: 0.9,
      prompt: "How do those sun catcher plates make electricity",
      temperature: 0.75,
      max_new_tokens: 128,
      min_new_tokens: -1
    }
  }
);

console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Import the client:

import replicate

Run ydideh810/cosmo-speak using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "ydideh810/cosmo-speak:fd8ed7161c14133273cfa8fdc60a10143a0107a7d4adeb495ba98f22257d965e",
    input={
        "debug": False,
        "top_k": 50,
        "top_p": 0.9,
        "prompt": "How do those sun catcher plates make electricity",
        "temperature": 0.75,
        "max_new_tokens": 128,
        "min_new_tokens": -1
    }
)

# The ydideh810/cosmo-speak model can stream output as it's running.
# The predict method returns an iterator, and you can iterate over that output.
for item in output:
    # https://replicate.com/ydideh810/cosmo-speak/api#output-schema
    print(item, end="")

To learn more, take a look at the guide on getting started with Python.

Run ydideh810/cosmo-speak using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "ydideh810/cosmo-speak:fd8ed7161c14133273cfa8fdc60a10143a0107a7d4adeb495ba98f22257d965e",
    "input": {
      "debug": false,
      "top_k": 50,
      "top_p": 0.9,
      "prompt": "How do those sun catcher plates make electricity",
      "temperature": 0.75,
      "max_new_tokens": 128,
      "min_new_tokens": -1
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

? How do those sun catcher plates make electricity? They are made of glass and use the same basic idea as solar panels. When the sun hits them, it makes electricity. The way they work is by using photovoltaic cells to convert light into electrical energy. The cells are made up of two layers of silicon with an insulating layer in between. When light shines on the top layer, it creates an electric current that flows through the silicon and out through a wire connected to a battery or other device that can use this power source. This process is called photovolta

{
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  "created_at": "2024-07-30T20:31:11.021000Z",
  "data_removed": false,
  "error": null,
  "id": "z997y3zpnnrgg0ch0nba33e128",
  "input": {
    "debug": false,
    "top_k": 50,
    "top_p": 0.9,
    "prompt": "How do those sun catcher plates make electricity",
    "temperature": 0.75,
    "max_new_tokens": 128,
    "min_new_tokens": -1
  },
  "logs": "Your formatted prompt is:\nHow do those sun catcher plates make electricity\ncorrect lora is already loaded\nOverall initialize_peft took 0.000\nExllama: False\nINFO 07-30 20:31:13 async_llm_engine.py:371] Received request 0: prompt: 'How do those sun catcher plates make electricity', sampling params: SamplingParams(n=1, best_of=1, presence_penalty=0.0, frequency_penalty=1.0, temperature=0.75, top_p=0.9, top_k=50, use_beam_search=False, length_penalty=1.0, early_stopping=False, stop=['</s>'], ignore_eos=False, max_tokens=128, logprobs=None, skip_special_tokens=True), prompt token ids: None.\nINFO 07-30 20:31:13 llm_engine.py:631] Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 1 reqs, Swapped: 0 reqs, Pending: 0 reqs, GPU KV cache usage: 0.0%, CPU KV cache usage: 0.0%\nINFO 07-30 20:31:16 async_llm_engine.py:111] Finished request 0.\nhostname: model-hp-73001d654114dad81ec65da3b834e2f6-888dc6d68-vnssc",
  "metrics": {
    "predict_time": 3.064159702,
    "total_time": 5.446791
  },
  "output": [
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    " can",
    " use",
    " this",
    " power",
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    ".",
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    "This",
    " process",
    " is",
    " called",
    " phot",
    "ov",
    "olta"
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Generated in

3.1 seconds

Tweak it Share Report

ydideh810 / cosmo-speak

Prediction

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Output

Prediction

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Prediction

Input

Output

Logs (d5en6kq75drgg0ch0n1a584bww)

Logs (6j6cn4jyy9rgp0ch0nb9g3n3z8)

Logs (z997y3zpnnrgg0ch0nba33e128)