Is it possible to run HuggingFaceTB/SmolLM2-135M-Instruct on workersai?

          try {
            console.log(`[BYPASS] Attempt 3: Using AI binding with raw bytes`);
            const aiTranscription = await this.env.AI.run('@cf/openai/whisper-large-v3-turbo', {
              audio: [...audioBytes], // Convert Uint8Array to regular array
              language: 'en',
            });
            
            

          try {
            console.log(`[BYPASS] Attempt 3: Using AI binding with raw bytes`);
            const aiTranscription = await this.env.AI.run('@cf/openai/whisper-large-v3-turbo', {
              audio: [...audioBytes], // Convert Uint8Array to regular array
              language: 'en',
            });
            
            

          try {
            console.log(`[BYPASS] Attempt 2: Using AI binding with base64`);
            const aiTranscription = await this.env.AI.run('@cf/openai/whisper-large-v3-turbo', {
              audio: base64Audio,
              language: 'en',
            });
                     

          try {
            console.log(`[BYPASS] Attempt 2: Using AI binding with base64`);
            const aiTranscription = await this.env.AI.run('@cf/openai/whisper-large-v3-turbo', {
              audio: base64Audio,
              language: 'en',
            });
                     

# Define the Durable Objects
[[durable_objects.bindings]]
name = "AUDIO_PROCESSOR"
class_name = "AudioProcessor"

# External DO binding for LLM worker
[[durable_objects.bindings]]
name = "LLM_PROCESSOR"
class_name = "LlmProcessor"
script_name = "llm"

# Add queue consumers with explicit handler functions
[[queues.consumers]]

[vars]


# Define the Durable Object class
[[migrations]]
tag = "v1"
new_classes = ["AudioProcessor"]

[[kv_namespaces]]


[[d1_databases]]


[[queues.consumers]]


[[queues.producers]]


[ai]
binding = "AI"

[observability]
enabled = true
head_sampling_rate = 1

# Define the Durable Objects
[[durable_objects.bindings]]
name = "AUDIO_PROCESSOR"
class_name = "AudioProcessor"

# External DO binding for LLM worker
[[durable_objects.bindings]]
name = "LLM_PROCESSOR"
class_name = "LlmProcessor"
script_name = "llm"

# Add queue consumers with explicit handler functions
[[queues.consumers]]

[vars]


# Define the Durable Object class
[[migrations]]
tag = "v1"
new_classes = ["AudioProcessor"]

[[kv_namespaces]]


[[d1_databases]]


[[queues.consumers]]


[[queues.producers]]


[ai]
binding = "AI"

[observability]
enabled = true
head_sampling_rate = 1

export class AudioProcessor {
  constructor(state, env) {
    this.state = state;
    this.env = env;
  }

export class AudioProcessor {
  constructor(state, env) {
    this.state = state;
    this.env = env;
  }

import { runWithTools } from '@cloudflare/ai-utils';
const model = '@cf/meta/llama-3.3-70b-instruct-fp8-fast';
const response = await runWithTools(
                    env.AI,
                    model,
                    {
                        messages: chatmessages,
                        tools: function_tools,
                        max_tokens: 8192,
                        temperature: 0.6,
                    },
                    { strictValidation: true, maxRecursiveToolRuns: 1, verbose: true, streamFinalResponse: true }
                );

import { runWithTools } from '@cloudflare/ai-utils';
const model = '@cf/meta/llama-3.3-70b-instruct-fp8-fast';
const response = await runWithTools(
                    env.AI,
                    model,
                    {
                        messages: chatmessages,
                        tools: function_tools,
                        max_tokens: 8192,
                        temperature: 0.6,
                    },
                    { strictValidation: true, maxRecursiveToolRuns: 1, verbose: true, streamFinalResponse: true }
                );

[
    {
        "name":"applyFilter",
        "description":"Function that applies the filter...",
        "parameters":{
            "type":"object",
            "properties":{
                "query":{
                    "type":"object",
                    "description":"The query object..."
                },
                "filterDesc":{
                    "type":"string",
                    "description":"Description, for the user, of the filter..."
                }
            },
            "required":["query","filterDesc"]
        }
    }
]

[
    {
        "name":"applyFilter",
        "description":"Function that applies the filter...",
        "parameters":{
            "type":"object",
            "properties":{
                "query":{
                    "type":"object",
                    "description":"The query object..."
                },
                "filterDesc":{
                    "type":"string",
                    "description":"Description, for the user, of the filter..."
                }
            },
            "required":["query","filterDesc"]
        }
    }
]

Error: 5006: Error: oneOf at '/' not met, 0 matches: required properties at '/' are 'prompt', Type mismatch of '/messages/0/content', 'array' not in 'string', Type mismatch of '/messages/1/content', 'array' not in 'string', required properties at '/functions/0' are 'name,code'

Error: 5006: Error: oneOf at '/' not met, 0 matches: required properties at '/' are 'prompt', Type mismatch of '/messages/0/content', 'array' not in 'string', Type mismatch of '/messages/1/content', 'array' not in 'string', required properties at '/functions/0' are 'name,code'

const API_BASE_URL = "https://api.cloudflare.com/client/v4/accounts/{myID}/ai/run/"
const API_AUTH_TOKEN = "{myTOKEN}" //process.env.API_AUTH_TOKEN;
const model = "@cf/meta/llama-2-7b-chat-int8"
const headers = {
        'Authorization':`Bearer ${API_AUTH_TOKEN}`, 
        //'Content-type':'application/type',
        "Access-Control-Allow-Origin": "*",
        "Access-Control-Allow-Methods": "GET,HEAD,POST,OPTIONS",
        "Access-Control-Max-Age": "86400"
    }

    if (!API_BASE_URL || !API_AUTH_TOKEN) {
        throw new Error('API credential is wrong or not configured from Github Action')
    }

    const inputs = [
        {'role':'system', 'content':systemPrompt},
        {'role':'user', 'content':userPrompt},
    ]

    const payload = {
        message: inputs
    }

    try {
        console.log("Requesting to LLM...")
        const response = await fetch(`${API_BASE_URL}${model}`, {
            method: 'POST', 
            headers:headers, 
            body: JSON.stringify(payload), 
            mode: 'no-cors'
        }
        );

        if (!response.ok) {
            throw new Error (`Error request from LLM: ${response.status}`)
        }
        console.log("Requesting completed. Waiting for output...")
        const output = await response.json(); // output
        console.log(output)
    }
    catch (error) {
        console.log("API Error", error);
        throw error;
    }

const API_BASE_URL = "https://api.cloudflare.com/client/v4/accounts/{myID}/ai/run/"
const API_AUTH_TOKEN = "{myTOKEN}" //process.env.API_AUTH_TOKEN;
const model = "@cf/meta/llama-2-7b-chat-int8"
const headers = {
        'Authorization':`Bearer ${API_AUTH_TOKEN}`, 
        //'Content-type':'application/type',
        "Access-Control-Allow-Origin": "*",
        "Access-Control-Allow-Methods": "GET,HEAD,POST,OPTIONS",
        "Access-Control-Max-Age": "86400"
    }

    if (!API_BASE_URL || !API_AUTH_TOKEN) {
        throw new Error('API credential is wrong or not configured from Github Action')
    }

    const inputs = [
        {'role':'system', 'content':systemPrompt},
        {'role':'user', 'content':userPrompt},
    ]

    const payload = {
        message: inputs
    }

    try {
        console.log("Requesting to LLM...")
        const response = await fetch(`${API_BASE_URL}${model}`, {
            method: 'POST', 
            headers:headers, 
            body: JSON.stringify(payload), 
            mode: 'no-cors'
        }
        );

        if (!response.ok) {
            throw new Error (`Error request from LLM: ${response.status}`)
        }
        console.log("Requesting completed. Waiting for output...")
        const output = await response.json(); // output
        console.log(output)
    }
    catch (error) {
        console.log("API Error", error);
        throw error;
    }