tomasmcm / evolved-seeker-1.3b

Source: TokenBender/evolvedSeeker_1_3 ✦ Quant: TheBloke/evolvedSeeker_1_3-AWQ ✦ A fine-tuned version of deepseek-ai/deepseek-coder-1.3b-base on 50k instructions for 3 epochs

Cold

Public
29 runs
T4
Paper
License

Iterate in playground

Run with an API

Playground API Examples README Versions

Input

prompt

*string

Shift + Return to add a new line

<|im_start|>system
You are a helpful coding assistant.<|im_end|>
<|im_start|>user
write a javascript program to reverse letters in each word in a sentence without reversing order of words in the sentence.<|im_end|>
<|im_start|>assistant<|im_start|>system
You are a helpful coding assistant.<|im_end|>
<|im_start|>user
write a javascript program to reverse letters in each word in a sentence without reversing order of words in the sentence.<|im_end|>
<|im_start|>assistant

Text prompt to send to the model.

max_tokens

integer

Maximum number of tokens to generate per output sequence.

Default: 128

presence_penalty

number

(minimum: -5, maximum: 5)

Float that penalizes new tokens based on whether they appear in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.

Default: 0

frequency_penalty

number

(minimum: -5, maximum: 5)

Float that penalizes new tokens based on their frequency in the generated text so far. Values > 0 encourage the model to use new tokens, while values < 0 encourage the model to repeat tokens.

Default: 0

temperature

number

(minimum: 0.01, maximum: 5)

Float that controls the randomness of the sampling. Lower values make the model more deterministic, while higher values make the model more random. Zero means greedy sampling.

Default: 0.8

top_p

number

(minimum: 0.01, maximum: 1)

Float that controls the cumulative probability of the top tokens to consider. Must be in (0, 1]. Set to 1 to consider all tokens.

Default: 0.95

top_k

integer

Integer that controls the number of top tokens to consider. Set to -1 to consider all tokens.

Default: -1

stop

string

Shift + Return to add a new line

List of strings that stop the generation when they are generated. The returned output will not contain the stop strings.

Run this model in Node.js with one line of code:

npx create-replicate --model=tomasmcm/evolved-seeker-1.3b

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

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

Run tomasmcm/evolved-seeker-1.3b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "tomasmcm/evolved-seeker-1.3b:04492f3607a921344a8173a727224dc1ea22518ea9d40b8231296afe29fb972a",
  {
    input: {
      stop: "<|im_end|>",
      top_k: 40,
      top_p: 0.95,
      prompt: "<|im_start|>system\nYou are a helpful coding assistant.<|im_end|>\n<|im_start|>user\nwrite a javascript program to reverse letters in each word in a sentence without reversing order of words in the sentence.<|im_end|>\n<|im_start|>assistant",
      max_tokens: 512,
      temperature: 0.7,
      presence_penalty: 0,
      frequency_penalty: 0
    }
  }
);

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

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run tomasmcm/evolved-seeker-1.3b using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "tomasmcm/evolved-seeker-1.3b:04492f3607a921344a8173a727224dc1ea22518ea9d40b8231296afe29fb972a",
    input={
        "stop": "<|im_end|>",
        "top_k": 40,
        "top_p": 0.95,
        "prompt": "<|im_start|>system\nYou are a helpful coding assistant.<|im_end|>\n<|im_start|>user\nwrite a javascript program to reverse letters in each word in a sentence without reversing order of words in the sentence.<|im_end|>\n<|im_start|>assistant",
        "max_tokens": 512,
        "temperature": 0.7,
        "presence_penalty": 0,
        "frequency_penalty": 0
    }
)
print(output)

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

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run tomasmcm/evolved-seeker-1.3b 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": "tomasmcm/evolved-seeker-1.3b:04492f3607a921344a8173a727224dc1ea22518ea9d40b8231296afe29fb972a",
    "input": {
      "stop": "<|im_end|>",
      "top_k": 40,
      "top_p": 0.95,
      "prompt": "<|im_start|>system\\nYou are a helpful coding assistant.<|im_end|>\\n<|im_start|>user\\nwrite a javascript program to reverse letters in each word in a sentence without reversing order of words in the sentence.<|im_end|>\\n<|im_start|>assistant",
      "max_tokens": 512,
      "temperature": 0.7,
      "presence_penalty": 0,
      "frequency_penalty": 0
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Here is a simple JavaScript program that achieves this: ```javascript function reverseWordsInSentence(sentence) { return sentence.split(' ').map(word => word.split('').reverse().join('')).join(' '); } console.log(reverseWordsInSentence("Hello world")); // Outputs: "olleH dlrow" ``` This function works by first splitting the sentence into an array of words. Then, for each word, it splits the word into an array of characters, reverses that array, then joins it back together into a word. Finally, it joins all the words back together into a sentence.

Generated in

4.9 seconds

Tweak it Report View full prediction

Run time and cost

This model runs on Nvidia T4 GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

evolvedSeeker-1_3

EvolvedSeeker v0.0.1 (First phase)

This model is a fine-tuned version of deepseek-ai/deepseek-coder-1.3b-base on 50k instructions for 3 epochs.

I have mostly curated instructions from evolInstruct datasets and some portions of glaive coder.

Around 3k answers were modified via self-instruct.

Collaborate or Consult me - Twitter, Discord

Recommended format is ChatML, Alpaca will work but take care of EOT token

Chat Model Inference

Use Gradio inference notebook here that can easily run in free colab - Gradio Inference Notebook

from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("TokenBender/evolvedSeeker_1_3", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("TokenBender/evolvedSeeker_1_3", trust_remote_code=True).cuda()
messages=[
    { 'role': 'user', 'content': "write a program to reverse letters in each word in a sentence without reversing order of words in the sentence."}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
# 32021 is the id of <|EOT|> token
outputs = model.generate(inputs, max_new_tokens=512, do_sample=False, top_k=50, top_p=0.95, num_return_sequences=1, eos_token_id=32021)
print(tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True))