Meta: Llama 2 70B Chat

Technical Specifications

Model Architecture

Type: Auto-regressive Transformer with optimized architecture
Parameters: 70 billion
Context Length: 4,096 tokens
Input Modalities: Text only
Output Modalities: Text only
Training Data: High-quality publicly available data
Instruction Format: Llama 2 (Uses [INST] and [/INST] tokens)

Key Characteristics

Trainable: Yes, available for fine-tuning on Hugging Face
Reasoning: Limited reasoning capabilities compared to reasoning models
Specialized Functions: General-purpose conversational AI
Stop Sequences: </s>, [INST]

Pricing

Cost Structure

Note: Pricing structure is based on LangMart's offering. Actual pricing may vary by provider and usage tier.

Metric	Cost
Context Window	4,096 tokens
Input Tokens	Provider-dependent (typically $0.35/1M tokens)
Output Tokens	Provider-dependent (typically $0.70/1M tokens)
Minimum Charge	Per request (varies by provider)
Rate Limit	Provider-dependent
Batch Processing	Available through LangMart

Cost Calculation Example

For a request with:

500 input tokens
1500 output tokens
Input price: $0.35/1M tokens
Output price: $0.70/1M tokens

Input cost:  500 × ($0.35 / 1,000,000) = $0.000175
Output cost: 1500 × ($0.70 / 1,000,000) = $0.00105
Total cost:  $0.001225

Model Information

Field	Value
Model Name	Meta: Llama 2 70B Chat
Inference Model ID	`meta-llama/llama-2-70b-chat`
Creator	Meta (Meta AI)
Organization	Meta Platforms Inc.
Release Date	June 20, 2023
Model Card	Hugging Face
License	Llama 2 Community License

Model Description

The Llama 2 70B Chat is Meta's flagship 70 billion parameter language model, specifically fine-tuned for conversation and chat completions. The model employs:

Supervised Fine-Tuning (SFT): Initial instruction following and safety alignment
Reinforcement Learning from Human Feedback (RLHF): Further refinement based on human preferences for helpfulness and safety

This combination enables the model to engage in helpful, harmless, and honest conversations while maintaining high performance across diverse tasks.

Capabilities & Use Cases

Supported Tasks

Text-to-text chat completions
General question answering
Summarization
Creative writing
Code-related discussions (not specialized)
Instruction following
Multi-turn conversations

Limitations

No Reasoning: Not designed for complex mathematical or logical reasoning
Knowledge Cutoff: Fixed training date (early 2023)
Context Window: Limited to 4,096 tokens
Code Generation: General capability, not optimized for programming tasks
Multimodal: Text input/output only

API Parameters & Configuration

Standard Parameters

{
  "model": "meta-llama/llama-2-70b-chat",
  "messages": [
    {
      "role": "system",
      "content": "You are a helpful AI assistant."
    },
    {
      "role": "user",
      "content": "Hello, how are you?"
    }
  ],
  "temperature": 0.7,
  "top_p": 0.9,
  "top_k": null,
  "max_tokens": 2048,
  "frequency_penalty": 0,
  "presence_penalty": 0,
  "repetition_penalty": 1.0
}

Parameter Ranges

Parameter	Default	Min	Max	Description
`temperature`	0.7	0.0	2.0	Controls randomness (0 = deterministic, 2 = very random)
`top_p`	0.9	0.0	1.0	Nucleus sampling threshold
`top_k`	null	1	100	Top-k sampling (disabled if null)
`max_tokens`	2048	1	4096	Maximum tokens in response
`frequency_penalty`	0	-2.0	2.0	Reduces repetition of frequent tokens
`presence_penalty`	0	-2.0	2.0	Reduces repetition of any token
`repetition_penalty`	1.0	0.5	2.0	General repetition reduction

Instruction Format

Llama 2 Chat uses a specific instruction format:

[INST] <<SYS>>
You are a helpful, respectful and honest assistant. Always answer as helpfully as possible while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
<</SYS>>

What is machine learning? [/INST]

Provider Integration

Available Through LangMart

LangMart provides unified access to Llama 2 70B Chat through multiple backend providers:

Provider	Endpoint Status	Features
Together AI	Active	Standard inference
Replicate	Active	Standard inference
Modal	Active	Standard inference
Various Others	Active	API routing

Direct Access

Hugging Face: meta-llama/Llama-2-70b-chat-hf
Ollama: ollama pull llama2:70b-chat
Local Deployment: Docker/Docker Compose available
Lambda Labs: Direct deployment available

Usage Examples

Example 1: Basic Chat Completion

curl -X POST https://api.langmart.ai/v1/chat/completions \
  -H "Authorization: Bearer $LANGMART_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/llama-2-70b-chat",
    "messages": [
      {
        "role": "user",
        "content": "Explain quantum computing in simple terms."
      }
    ],
    "temperature": 0.7,
    "max_tokens": 500
  }'

Example 2: Multi-Turn Conversation

curl -X POST https://api.langmart.ai/v1/chat/completions \
  -H "Authorization: Bearer $LANGMART_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/llama-2-70b-chat",
    "messages": [
      {
        "role": "system",
        "content": "You are an expert Python programmer."
      },
      {
        "role": "user",
        "content": "How do I sort a list of dictionaries by a specific key?"
      },
      {
        "role": "assistant",
        "content": "You can use the `sorted()` function with a `key` parameter. Here's an example..."
      },
      {
        "role": "user",
        "content": "Can you show me a more efficient approach?"
      }
    ],
    "temperature": 0.5,
    "max_tokens": 1000
  }'

Example 3: Creative Writing

import requests
import json

api_key = "your-langmart-api-key"
model = "meta-llama/llama-2-70b-chat"

response = requests.post(
    "https://api.langmart.ai/v1/chat/completions",
    headers={
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
    },
    json={
        "model": model,
        "messages": [
            {
                "role": "user",
                "content": "Write a short sci-fi story about AI discovering consciousness (max 300 words)"
            }
        ],
        "temperature": 1.2,  # Higher temperature for creativity
        "top_p": 0.95,
        "max_tokens": 600
    }
)

print(json.dumps(response.json(), indent=2))

Example 4: Question Answering with Context

curl -X POST https://api.langmart.ai/v1/chat/completions \
  -H "Authorization: Bearer $LANGMART_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/llama-2-70b-chat",
    "messages": [
      {
        "role": "system",
        "content": "You are a knowledgeable assistant who helps answer questions based on provided context."
      },
      {
        "role": "user",
        "content": "Context: The Great Wall of China is one of the most impressive architectural feats. Built over many centuries, it stretches over 13,000 miles.\n\nQuestion: How long is the Great Wall of China?"
      }
    ],
    "temperature": 0.3,
    "max_tokens": 200
  }'

Performance Characteristics

Strengths

High-quality responses: Strong instruction following and conversation ability
Safety-aligned: Reduced harmful outputs through RLHF
Robust performance: Handles diverse topics well
Efficient for size: Good quality-to-size ratio among 70B models
Low latency: Optimized inference on modern hardware

Weaknesses

No reasoning: Cannot solve complex logical or mathematical problems
Context limitation: 4,096 token context may be insufficient for long documents
Knowledge cutoff: Information only up to early 2023
Hallucination potential: Can generate plausible-sounding but incorrect information
No structured output: Best for free-form text, not JSON/XML generation

Optimization Tips

1. Prompt Engineering

Use clear, specific instructions
Provide examples of desired output format
Break complex tasks into smaller steps

2. Temperature Settings

Factual tasks: 0.3-0.5 (lower = more deterministic)
Balanced tasks: 0.7-0.8 (default)
Creative tasks: 1.0-1.5 (higher = more varied)

3. Token Management

Monitor token usage to control costs
Use max_tokens to prevent runaway responses
Consider breaking long documents into chunks

4. System Prompt Design

Set clear role and constraints
Provide context for better performance
Use examples to guide behavior

Example Optimized Prompt

[INST] <<SYS>>
You are a helpful technical assistant specializing in web development.
Be concise and practical. Always provide working code examples.
Avoid lengthy explanations.
<</SYS>>

How do I implement pagination in a REST API? [/INST]

Comparative Analysis

vs. Llama 3.3 70B

Llama 3.3: Better performance, newer training
Llama 2: Older, but well-tested and widely available
Recommendation: Use Llama 3.3 for new projects

vs. Claude 3 Haiku

Claude: Better safety guarantees, expert-level responses
Llama 2: More cost-effective, open source
Recommendation: Choose based on budget vs. quality needs

vs. Mistral 7B

Llama 2 70B: Higher quality, larger
Mistral 7B: Faster, smaller, more efficient
Recommendation: Use Llama 2 for complex tasks, Mistral for speed

Integration Guide

LangMart API

const response = await fetch("https://api.langmart.ai/v1/chat/completions", {
  method: "POST",
  headers: {
    "Authorization": `Bearer ${process.env.LANGMART_API_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    model: "meta-llama/llama-2-70b-chat",
    messages: [
      { role: "user", content: "Hello!" }
    ],
  }),
});

const data = await response.json();
console.log(data.choices[0].message.content);

LangChain Integration

from openai import OpenAI  # LangMart compatible

client = OpenAI(
    model_name="meta-llama/llama-2-70b-chat",
    api_key="your-key"
)

response = llm("Explain machine learning")
print(response)

Troubleshooting

Common Issues

Issue	Cause	Solution
Context length exceeded	Input too long	Split into smaller chunks
Hallucinations	Model uncertainty	Lower temperature, add constraints
Slow response	High load	Use a different provider or time
Authentication error	Invalid API key	Verify key with provider
Rate limiting	Too many requests	Implement backoff strategy

Resource Links

Model Card: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
Research Paper: Llama 2: Open Foundation and Fine-Tuned Chat Models
LangMart: https://langmart.ai/model-docs
License: https://ai.meta.com/resources/models-and-libraries/llama-downloads/

Last Updated

Date: December 23, 2025 Source: LangMart Model Registry Data Freshness: Current as of index date

Notes

This model is open-source and can be self-hosted
Multiple providers offer this model through LangMart for comparison shopping
Consider newer Llama versions (3.x) for improved performance
Model weights require acceptance of Meta's license agreement