llama-2-7b-chat-hf-lora

Beta

Text Generation • Meta

@cf/meta-llama/llama-2-7b-chat-hf-lora

  • Cloudflare-hosted
  • LoRA

This is a Llama2 base model that Cloudflare dedicated for inference with LoRA adapters. Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format.

Model Info
Context Window ↗ 8,192 tokens
LoRA Yes
Beta Yes

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.

Launch the LLM Playground

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/llama-2-7b-chat-hf-lora", {
      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/llama-2-7b-chat-hf-lora", { 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/llama-2-7b-chat-hf-lora",
    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/llama-2-7b-chat-hf-lora \
  -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

StreamingInput Open Download

StreamingOutput Open Download

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