Llama 2 7B 32K on Together AI

Together Llama 2 · Together.ai

DeprecatedRetired August 22, 2024
Open Weights

Last refreshed 2026-11-11. Next refresh: weekly.

Why use Llama 2 7B 32K on Together AI?

Together AI offers Llama 2 7B 32K with pay-as-you-go pricing at $0.20/1M input tokens. Together AI is a platform for running open-source and proprietary LLMs with fast serverless and dedicated endpoints at competitive inference pricing.

Input / 1M
$0.20
Output / 1M
$0.20
Cache
Not sourced
Batch
Not sourced

Setup recipe

Python + curl
Install
pip install together
Auth
export TOGETHER_API_KEY=...
Call
from together import Together
client = Together()  # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
    model="togethercomputer/LLaMA-2-7B-32K",
Model ID
togethercomputer/LLaMA-2-7B-32K

Request example

from together import Together

client = Together()  # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
    model="togethercomputer/LLaMA-2-7B-32K",
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)

Gotchas

  • Use provider model ID "togethercomputer/LLaMA-2-7B-32K", not the LLMReference slug "llama-2-7b-32k".
  • Together uses "organization/model-name" format, e.g. "meta-llama/Llama-4-Scout-17B-16E-Instruct" or "Qwen/QwQ-32B". See the Together model catalog for the exact ID.
  • The examples expect TOGETHER_API_KEY; rename it only if your application config maps the new variable.

Pricing

TypePrice (per 1M)
Input tokens$0.20
Output tokens$0.20

Capabilities

Structured Outputs

About Llama 2 7B 32K

LLaMA-2-7B-32K is an open-source language model engineered by Together, derived from Meta's LLaMA-2 7B. It boasts a unique extended context length of up to 32,000 tokens, which enhances its ability to tackle tasks involving long-range context, such as multi-document question answering and lengthy text summarization. The model integrates optimizations, including FlashAttention-2, to boost inference and training efficiency. It combines pre-training with instruction tuning data for improved task performance and offers fine-tuning examples for specialized applications, like book summarization or multi-document Q&A.

Get Started

Model Specs

Released2023-07-18
Parameters7B
Context32K
ArchitectureDecoder Only
Knowledge cutoff2022-09