llama-3.2-1b-instruct
Text Generation • Meta
@cf/meta/llama-3.2-1b-instruct
- Cloudflare-hosted
The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks.
| Model Info | |
|---|---|
| Context Window ↗ | 60,000 tokens |
| Terms and License | link ↗ |
| Unit Pricing | $0.027 per M input tokens, $0.201 per M output tokens |
Playground
Try out this model with Workers AI LLM Playground. It does not require any setup or authentication and is an instant way to preview and test a model directly in the browser.
Usage
export interface Env {
AI: Ai;
}
export default {
async fetch(request, env): Promise<Response> {
const messages = [
{ role: "system", content: "You are a friendly assistant" },
{
role: "user",
content: "What is the origin of the phrase Hello, World",
},
];
const stream = await env.AI.run("@cf/meta/llama-3.2-1b-instruct", {
messages,
stream: true,
});
return new Response(stream, {
headers: { "content-type": "text/event-stream" },
});
},
} satisfies ExportedHandler<Env>;
export interface Env {
AI: Ai;
}
export default {
async fetch(request, env): Promise<Response> {
const messages = [
{ role: "system", content: "You are a friendly assistant" },
{
role: "user",
content: "What is the origin of the phrase Hello, World",
},
];
const response = await env.AI.run("@cf/meta/llama-3.2-1b-instruct", { messages });
return Response.json(response);
},
} satisfies ExportedHandler<Env>;
import os
import requests
ACCOUNT_ID = "your-account-id"
AUTH_TOKEN = os.environ.get("CLOUDFLARE_AUTH_TOKEN")
prompt = "Tell me all about PEP-8"
response = requests.post(
f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/meta/llama-3.2-1b-instruct",
headers={"Authorization": f"Bearer {AUTH_TOKEN}"},
json={
"messages": [
{"role": "system", "content": "You are a friendly assistant"},
{"role": "user", "content": prompt}
]
}
)
result = response.json()
print(result)
curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run/@cf/meta/llama-3.2-1b-instruct \
-X POST \
-H "Authorization: Bearer $CLOUDFLARE_AUTH_TOKEN" \
-d '{ "messages": [{ "role": "system", "content": "You are a friendly assistant" }, { "role": "user", "content": "Why is pizza so good" }]}'
OpenAI compatible endpoints
Workers AI also supports OpenAI compatible API endpoints for /v1/chat/completions and /v1/embeddings. For more details, refer to Configurations .
Parameters
Input
prompt
stringrequiredminLength: 1The input text prompt for the model to generate a response.
lora
stringName of the LoRA (Low-Rank Adaptation) model to fine-tune the base model.
▶response_format{}
object
raw
booleandefault: falseIf true, a chat template is not applied and you must adhere to the specific model's expected formatting.
stream
booleandefault: falseIf true, the response will be streamed back incrementally using SSE, Server Sent Events.
max_tokens
integerdefault: 256The maximum number of tokens to generate in the response.
temperature
numberdefault: 0.6minimum: 0maximum: 5Controls the randomness of the output; higher values produce more random results.
top_p
numberminimum: 0.001maximum: 1Adjusts the creativity of the AI's responses by controlling how many possible words it considers. Lower values make outputs more predictable; higher values allow for more varied and creative responses.
top_k
integerminimum: 1maximum: 50Limits the AI to choose from the top 'k' most probable words. Lower values make responses more focused; higher values introduce more variety and potential surprises.
seed
integerminimum: 1maximum: 9999999999Random seed for reproducibility of the generation.
repetition_penalty
numberminimum: 0maximum: 2Penalty for repeated tokens; higher values discourage repetition.
frequency_penalty
numberminimum: -2maximum: 2Decreases the likelihood of the model repeating the same lines verbatim.
presence_penalty
numberminimum: -2maximum: 2Increases the likelihood of the model introducing new topics.
Output
<details>
<summary>Synchronous — Send a request and receive a complete response</summary>
response
<code>string</code>The generated text response from the model
▶usage{}
<code>object</code>Usage statistics for the inference request
▶tool_calls[]
<code>array</code>An array of tool calls requests made during the response generation
</details>
<details>
<summary>Streaming — Send a request with stream: true and receive server-sent events</summary>
type
<code>string</code>
format
<code>binary</code>
</details>
API Schemas (Raw)
SynchronousInput Open Download
SynchronousOutput Open Download
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description: The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. title: llama-3.2-3b-instruct image: https://developers.cloudflare.com/og-docs.png
Documentation Index
Fetch the complete documentation index at: https://developers.cloudflare.com/llms.txt
Use this file to discover all available pages before exploring further.

