M

Meta: Llama 3.2 1B Instruct

Meta
Vision
60K
Context
$0.0270
Input /1M
$0.2000
Output /1M
N/A
Max Output
Run API Call Open in Chat

Meta: Llama 3.2 1B Instruct

Model Overview

Full Name: Meta: Llama 3.2 1B Instruct Model ID: meta-llama/llama-3.2-1b-instruct Provider: LangMart (routes to Cloudflare) Created: September 25, 2024 Model Type: Language Model - Instruction-tuned Parameters: 1 billion

Description

Llama 3.2 1B is a 1-billion-parameter language model optimized for efficiently performing natural language tasks such as summarization, dialogue, and multilingual text analysis. With its smaller size, this model allows for efficient operation in low-resource environments while maintaining strong task performance. Supporting eight core languages (English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai), it's ideal for businesses and developers seeking lightweight, fast inference with quality instruction-following capabilities.

Technical Specifications

Context & Output Limits

  • Maximum Context Window: 60,000 tokens
  • Maximum Output: 60,000 tokens (configured per provider)

Training & Architecture

  • Training Data: Trained on 9 trillion tokens
  • Quantization: Standard precision (fp32/bf16)
  • Languages Supported: 8 core languages
    • English
    • German
    • French
    • Italian
    • Portuguese
    • Hindi
    • Spanish
    • Thai

Pricing

Metric Price per Million Tokens
Context Window 60,000 tokens
Input Tokens $0.027
Output Tokens $0.20

Context Pricing: Base pricing as shown above; cache pricing not specified

Supported Parameters

The following parameters are supported for inference requests:

  • max_tokens - Maximum tokens to generate
  • temperature - Sampling temperature (0.0-2.0)
  • top_p - Nucleus sampling parameter
  • top_k - Top-k sampling parameter
  • seed - Random seed for reproducibility
  • repetition_penalty - Penalize repetitive content
  • frequency_penalty - Adjust token frequency penalties
  • presence_penalty - Penalize token presence

Use Cases

This model is particularly well-suited for:

  1. Lightweight Inference: Applications requiring fast responses with minimal computational resources
  2. Multilingual Support: Services supporting multiple languages without large model overhead
  3. Edge Deployment: Running on mobile devices, IoT, or resource-constrained environments
  4. Cost-Efficient Processing: High-volume inference where API costs are critical
  5. Real-time Chat: Interactive applications requiring low latency
  6. Text Summarization: Quick abstractive summarization of documents
  7. Dialogue Systems: Conversational AI with limited compute

Provider Details

Cloudflare (Primary Provider)

Status: Available on LangMart Uptime: 100.0% (current) Quantization: Standard

Performance Metrics:

  • Average Latency: 0.34 seconds
  • Throughput: 391.0 tokens/second
  • Uptime (24h): 100.0%

Data Policy:

  • Prompt Training: False (not used for training)
  • Prompt Logging: Retained for unknown period
  • Moderation: Responsibility of developer

Performance Statistics

Real-time Metrics

Metric Value
Average Latency 0.34s
Average Throughput 391.0 tps
Uptime 100.0%
E2E Latency 1.15s

Usage Patterns

The model sees active usage across:

  • Top Application: Janitor AI (14.2M tokens this month)
  • Use Cases: Character chat, creative writing, dialogue generation
  • Peak Usage: Consistent demand for lightweight inference

Larger Models

  • Llama 3.3 8B Instruct - Lightweight variant of Llama 3.3 70B for quick responses
  • Llama 3.3 70B Instruct - Full multilingual model with 8 language support
  • Llama 3.1 405B Instruct - Flagship 400B parameter model with 128k context
  • Llama 3.1 70B Instruct - Larger, more capable instruction-tuned variant

Similar Size Models

  • Llama 3.2 3B Instruct - 3-billion-parameter version with extended context
  • Llama 3.2 90B Vision Instruct - Multimodal version with 90B parameters
  • Llama 3.2 11B Vision Instruct - Smaller multimodal variant with 11B parameters

Legacy Models

  • Llama 3.1 8B Instruct - Previous generation 8B instruction-tuned model
  • Llama 2 70B Chat - Earlier generation 70B chat model
  • CodeLlama 34B Instruct - Specialized code generation model

Model Weights & Resources

API Integration

Example Request (cURL)

curl https://api.langmart.ai/v1/chat/completions \
  -H "Authorization: Bearer $LANGMART_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/llama-3.2-1b-instruct",
    "messages": [
      {"role": "user", "content": "Summarize quantum computing in 50 words."}
    ],
    "max_tokens": 100,
    "temperature": 0.7
  }'

LangMart SDK Example

import OpenAI from "openai"  // LangMart compatible;

const client = new OpenAI({
  apiKey: process.env.LANGMART_API_KEY,
});

const stream = await client.chat.completions.create({
  model: "meta-llama/llama-3.2-1b-instruct",
  messages: [
    {
      role: "user",
      content: "What are the key benefits of machine learning?",
    },
  ],
  temperature: 0.7,
  max_tokens: 256,
});

for await (const chunk of stream) {
  const content = chunk.choices[0]?.delta?.content;
  if (content) process.stdout.write(content);
}

Key Advantages

  1. Extreme Efficiency: 1B parameters enables deployment on constrained hardware
  2. Affordable: Lowest cost point for quality instruction-following
  3. Multilingual: Supports 8 languages natively
  4. Fast Inference: 391 tokens/second throughput for real-time applications
  5. Proven Quality: Part of successful Llama 3 family
  6. Accessible: Great for developers getting started with large language models

Limitations & Considerations

  1. Context Limitations: 60K context window is smaller than larger models
  2. Reasoning Capacity: Limited ability for complex multi-step reasoning compared to larger variants
  3. Domain Expertise: May not excel in specialized domains requiring deeper knowledge
  4. Output Consistency: May require more guidance through prompting for complex tasks

Last Updated: December 24, 2024 Data Source: LangMart Status: Active & Available