CodeGen is a family of autoregressive language models for program synthesis, trained on a Python programming language dataset. The models are capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them. They are intended for and best at program synthesis, that is, generating executable code given English prompts. The evaluation results show that CodeGen achieves state-of-the-art performance on two code generation benchmarks, HumanEval and MTPB.

You can use cURL or any other http client to run inferences:

```bash
curl -X POST \
    -d '{"input": "I have this dream"}'  \
    -H "Authorization: bearer $(deepctl auth token)"  \
    -H 'Content-Type: application/json'  \
    'https://api.deepinfra.com/v1/inference/Salesforce/codegen-16B-mono'
```

which will give you back something similar to:

```json
{
  "results": [
    {
      "generated_text": "I have this dream about the day I got a job at a tech company. I just woke up on a plane. I sat down on the floor and started getting work done. After getting up around 6 p.m., I looked around and"
    }
  ],
  "num_tokens": 42,
  "num_input_tokens": 100,
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0
  }
}

```


You can use our command-line tool [deepctl](/docs/getting-started) to run
inferences:

```bash
deepctl infer \
    -m 'Salesforce/codegen-16B-mono'  \
    -i 'input=I have this dream'
```

which will give you back something similar to:

```json
{
  "results": [
    {
      "generated_text": "I have this dream about the day I got a job at a tech company. I just woke up on a plane. I sat down on the floor and started getting work done. After getting up around 6 p.m., I looked around and"
    }
  ],
  "num_tokens": 42,
  "num_input_tokens": 100,
  "request_id": null,
  "inference_status": {
    "status": "unknown",
    "runtime_ms": 0,
    "cost": 0.0,
    "tokens_generated": 0,
    "tokens_input": 0
  }
}

```


input

maximum length of the newly generated generated text

max_new_tokens

temperature to use for sampling. 0 means the output is deterministic. Values greater than 1 encourage more diversity

temperature

Sample from the set of tokens with highest probability such that sum of probabilies is higher than p. Lower values focus on the most probable tokens.Higher values sample more low-probability tokens

top_p

Sample from the best k (number of) tokens. 0 means off

top_k

repetition penalty. Value of 1 means no penalty, values greater than 1 discourage repetition, smaller than 1 encourage repetition.

repetition_penalty

Up to 4 strings that will terminate generation immediately

stop

Number of output sequences to return. Incompatible with streaming

num_responses

The webhook to call when inference is done, by default you will get the output in the response of your inference request

webhook

Whether to stream tokens, by default it will be false, currently only supported for Llama 2 text generation models, token by token updates will be sent over SSE

stream

Input

Max New Tokens

Num Responses

Repetition Penalty

Stop

Stream

Temperature

Top K

Top P

Webhook

TextGenerationIn

I have this dream about the day I got a job at a tech company. I just woke up on a plane. I sat down on the floor and started getting work done. After getting up around 6 p.m., I looked around and