البدء مع ALLaM-7B-Instruct-preview

# -*- coding: utf-8 -*-
"""
Example usage for the ALLaM-AI/ALLaM-7B-Instruct-preview model from Hugging Face.
 
This script demonstrates how to load the model and tokenizer using the
transformers library and generate text based on a sample prompt.
 
Requirements:
- transformers>=4.40.1
- torch
- A CUDA-enabled GPU is highly recommended for performance.
"""
 
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
 
# --- Configuration ---
MODEL_NAME = "ALLaM-AI/ALLaM-7B-Instruct-preview"
# Set device to CUDA if available, otherwise CPU (will be very slow on CPU)
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
print(f"Using device: {DEVICE}")
 
# --- Load Model and Tokenizer ---
try:
    print(f"Loading model: {MODEL_NAME}...")
    # Consider adding torch_dtype=torch.bfloat16 if memory is an issue and GPU supports it
    allam_model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
    print(f"Loading tokenizer: {MODEL_NAME}...")
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    print("Model and tokenizer loaded successfully.")
 
    # Move model to the selected device
    allam_model = allam_model.to(DEVICE)
 
except Exception as e:
    print(f"Error loading model or tokenizer: {e}")
    print("Please ensure you have the necessary libraries installed and are logged in to Hugging Face if required.")
    exit(1)
 
# --- Prepare Input ---
# Example prompt (Arabic)
messages = [
    {"role": "user", "content": "كيف أجهز كوب شاهي؟"}, # "How do I prepare a cup of tea?"
]
 
# Apply the chat template (handles formatting for the model)
# Note: The model card mentions the system prompt is integrated here.
# You could potentially add a system message like:
# messages = [
#     {"role": "system", "content": "You are ALLaM, a bilingual English and Arabic AI assistant."},
#     {"role": "user", "content": "كيف أجهز كوب شاهي؟"},
# ]
try:
    print("Applying chat template...")
    # tokenize=False first to get the formatted string, then tokenize
    formatted_input_string = tokenizer.apply_chat_template(messages, tokenize=False)
    print(f"Formatted input string:\n{formatted_input_string}")
 
    print("Tokenizing input...")
    inputs = tokenizer(formatted_input_string, return_tensors='pt', return_token_type_ids=False)
 
    # Move inputs to the selected device
    inputs = {k: v.to(DEVICE) for k, v in inputs.items()}
    print("Input prepared for the model.")
 
except Exception as e:
    print(f"Error preparing input: {e}")
    exit(1)
 
# --- Generate Response ---
print("Generating response...")
try:
    # Generation parameters (adjust as needed)
    generation_params = {
        "max_new_tokens": 4096, # Max tokens for the *newly generated* text
        "do_sample": True,     # Use sampling for more creative output
        "top_k": 50,           # Consider only the top K most likely tokens
        "top_p": 0.95,         # Use nucleus sampling (cumulative probability)
        "temperature": 0.6     # Controls randomness (lower = more deterministic)
    }
    print(f"Generation parameters: {generation_params}")
 
    with torch.no_grad(): # Disable gradient calculations for inference
        response_ids = allam_model.generate(**inputs, **generation_params)
 
    print("Decoding response...")
    # Decode the generated token IDs back to text
    # response_ids[0] accesses the first sequence in the batch
    # skip_special_tokens=True removes tokens like <bos>, <eos>
    decoded_response = tokenizer.batch_decode(response_ids, skip_special_tokens=True)[0]
 
    # Often the decoded response includes the input prompt, we might want to remove it.
    # This simple approach assumes the response starts exactly with the formatted input.
    # More robust methods might be needed depending on the model's exact output format.
    if decoded_response.startswith(formatted_input_string):
         final_output = decoded_response[len(formatted_input_string):].strip()
    else:
         # Fallback if the prompt isn't exactly at the start (might happen with some templates/models)
         # This might still include parts of the prompt depending on the tokenizer/template behavior.
         # A more robust way might involve finding the specific turn separator used by the template.
         print("Warning: Could not cleanly separate prompt from response. Displaying full decoded output.")
         final_output = decoded_response # Show the full thing if separation fails
 
    print("\n--- Generated Response ---")
    print(final_output)
    print("--------------------------\n")
 
except Exception as e:
    print(f"Error during generation or decoding: {e}")
    exit(1)
 
print("Script finished successfully.")

python allam_example.py

/**
 * Example JavaScript usage for interacting with the ALLaM model hosted on Hugging Face Spaces.
 *
 * IMPORTANT: This script assumes you have deployed the ALLaM model within a
 * backend application (e.g., using Python FastAPI/Gradio and the `transformers` library)
 * on Hugging Face Spaces. You need to replace the placeholder URL below
 * with the actual public URL of your deployed Space's API endpoint.
 */
 
// !!! REPLACE THIS WITH YOUR ACTUAL HUGGING FACE SPACE API ENDPOINT URL !!!
// It might look something like: https://your-username-your-space-name.hf.space/generate
const HF_SPACE_API_URL = "https://YOUR_USERNAME-YOUR_SPACE_NAME.hf.space/generate"; // Placeholder URL
 
// Example prompt
const userPrompt = "كيف أجهز كوب شاهي؟";
 
async function generateTextViaHFSpace(promptText) {
  console.log(`Sending prompt to HF Space: "${promptText}" at ${HF_SPACE_API_URL}`);
 
  if (HF_SPACE_API_URL.includes("YOUR_USERNAME-YOUR_SPACE_NAME")) {
    console.error("Error: Please replace the placeholder HF_SPACE_API_URL with your actual Space endpoint URL.");
    return null;
  }
 
  try {
    const response = await fetch(HF_SPACE_API_URL, {
      method: "POST",
      headers: {
        "Content-Type": "application/json",
        // "Authorization": "Bearer YOUR_HF_TOKEN_IF_NEEDED", // Add if your Space is private
      },
      body: JSON.stringify({ prompt: promptText }), // Adjust payload as needed by your API
    });
 
    if (!response.ok) {
      let errorDetails = `HTTP error! status: ${response.status}`;
      try { const errorBody = await response.json(); errorDetails += `, Body: ${JSON.stringify(errorBody)}`; }
      catch (e) { const textBody = await response.text(); errorDetails += `, Body: ${textBody}`; }
      throw new Error(errorDetails);
    }
 
    const data = await response.json();
    console.log("Received response from HF Space.");
    // Adjust the key based on what your Space API returns
    return data.generated_text || data.response || data;
 
  } catch (error) {
    console.error("Error calling Hugging Face Space API:", error);
    throw error;
  }
}
 
// Execute
(async () => {
  try {
    const generatedText = await generateTextViaHFSpace(userPrompt);
    if (generatedText !== null) {
        console.log("\n--- Generated Response (from HF Space) ---");
        console.log(generatedText);
        console.log("------------------------------------------\n");
    }
  } catch (error) {
    console.error("Failed to get generation from Hugging Face Space.");
  }
})();
 
// To run this (assuming Node.js):
// 1. Deploy ALLaM in a Hugging Face Space with an API endpoint.
// 2. Update the HF_SPACE_API_URL constant in this script.
// 3. Run `node allam_hf_space_example.js`.