Text Generation (original) (raw)
Generate text based on a prompt.
If you are interested in a Chat Completion task, which generates a response based on a list of messages, check out the chat-completion task.
For more details about the text-generation task, check out its dedicated page! You will find examples and related materials.
Recommended models
- google/gemma-2-2b-it: A text-generation model trained to follow instructions.
- meta-llama/Meta-Llama-3.1-8B-Instruct: Very powerful text generation model trained to follow instructions.
- microsoft/phi-4: Powerful text generation model by Microsoft.
- Qwen/Qwen2.5-Coder-32B-Instruct: Text generation model used to write code.
- deepseek-ai/DeepSeek-R1: Powerful reasoning based open large language model.
Explore all available models and find the one that suits you best here.
Using the API
from huggingface_hub import InferenceClient
client = InferenceClient( provider="hf-inference", api_key="hf_xxxxxxxxxxxxxxxxxxxxxxxx", )
completion = client.chat.completions.create( model="Qwen/Qwen3-235B-A22B", messages=""Can you please let us know more details about your "", max_tokens=512, )
print(completion.choices[0].message)
API specification
Request
| Headers | ||
|---|---|---|
| authorization | string | Authentication header in the form 'Bearer: hf_****' when hf_**** is a personal user access token with “Inference Providers” permission. You can generate one from your settings page. |
| Payload | ||
|---|---|---|
| inputs* | string | |
| parameters | object | |
| adapter_id | string | Lora adapter id |
| best_of | integer | Generate best_of sequences and return the one if the highest token logprobs. |
| decoder_input_details | boolean | Whether to return decoder input token logprobs and ids. |
| details | boolean | Whether to return generation details. |
| do_sample | boolean | Activate logits sampling. |
| frequency_penalty | number | The parameter for frequency penalty. 1.0 means no penalty Penalize new tokens based on their existing frequency in the text so far, decreasing the model’s likelihood to repeat the same line verbatim. |
| grammar | unknown | One of the following: |
| (#1) | object | |
| type* | enum | Possible values: json. |
| value* | unknown | A string that represents a JSON Schema. JSON Schema is a declarative language that allows to annotate JSON documents with types and descriptions. |
| (#2) | object | |
| type* | enum | Possible values: regex. |
| value* | string | |
| max_new_tokens | integer | Maximum number of tokens to generate. |
| repetition_penalty | number | The parameter for repetition penalty. 1.0 means no penalty. See this paper for more details. |
| return_full_text | boolean | Whether to prepend the prompt to the generated text |
| seed | integer | Random sampling seed. |
| stop | string[] | Stop generating tokens if a member of stop is generated. |
| temperature | number | The value used to module the logits distribution. |
| top_k | integer | The number of highest probability vocabulary tokens to keep for top-k-filtering. |
| top_n_tokens | integer | The number of highest probability vocabulary tokens to keep for top-n-filtering. |
| top_p | number | Top-p value for nucleus sampling. |
| truncate | integer | Truncate inputs tokens to the given size. |
| typical_p | number | Typical Decoding mass See Typical Decoding for Natural Language Generation for more information. |
| watermark | boolean | Watermarking with A Watermark for Large Language Models. |
| stream | boolean |
Response
Output type depends on the stream input parameter. If stream is false (default), the response will be a JSON object with the following fields:
| Body | ||
|---|---|---|
| details | object | |
| best_of_sequences | object[] | |
| finish_reason | enum | Possible values: length, eos_token, stop_sequence. |
| generated_text | string | |
| generated_tokens | integer | |
| prefill | object[] | |
| id | integer | |
| logprob | number | |
| text | string | |
| seed | integer | |
| tokens | object[] | |
| id | integer | |
| logprob | number | |
| special | boolean | |
| text | string | |
| top_tokens | array[] | |
| id | integer | |
| logprob | number | |
| special | boolean | |
| text | string | |
| finish_reason | enum | Possible values: length, eos_token, stop_sequence. |
| generated_tokens | integer | |
| prefill | object[] | |
| id | integer | |
| logprob | number | |
| text | string | |
| seed | integer | |
| tokens | object[] | |
| id | integer | |
| logprob | number | |
| special | boolean | |
| text | string | |
| top_tokens | array[] | |
| id | integer | |
| logprob | number | |
| special | boolean | |
| text | string | |
| generated_text | string |
If stream is true, generated tokens are returned as a stream, using Server-Sent Events (SSE). For more information about streaming, check out this guide.
| Body | ||
|---|---|---|
| details | object | |
| finish_reason | enum | Possible values: length, eos_token, stop_sequence. |
| generated_tokens | integer | |
| input_length | integer | |
| seed | integer | |
| generated_text | string | |
| index | integer | |
| token | object | |
| id | integer | |
| logprob | number | |
| special | boolean | |
| text | string | |
| top_tokens | object[] | |
| id | integer | |
| logprob | number | |
| special | boolean | |
| text | string |