10 posts tagged “long-context”
Tracking how LLMs are getting better at accepting large amounts of input.
2025
In my experience with AI coding, very large context windows aren't useful in practice. Every model seems to get confused when you feed them more than ~25-30k tokens. The models stop obeying their system prompts, can't correctly find/transcribe pieces of code in the context, etc.
Developing aider, I've seen this problem with gpt-4o, Sonnet, DeepSeek, etc. Many aider users report this too. It's perhaps the #1 problem users have, so I created a dedicated help page.
Very large context may be useful for certain tasks with lots of "low value" context. But for coding, it seems to lure users into a problematic regime.
Qwen2.5-1M: Deploy Your Own Qwen with Context Length up to 1M Tokens (via) Very significant new release from Alibaba's Qwen team. Their openly licensed (sometimes Apache 2, sometimes Qwen license, I've had trouble keeping up) Qwen 2.5 LLM previously had an input token limit of 128,000 tokens. This new model increases that to 1 million, using a new technique called Dual Chunk Attention, first described in this paper from February 2024.
They've released two models on Hugging Face: Qwen2.5-7B-Instruct-1M and Qwen2.5-14B-Instruct-1M, both requiring CUDA and both under an Apache 2.0 license.
You'll need a lot of VRAM to run them at their full capacity:
VRAM Requirement for processing 1 million-token sequences:
- Qwen2.5-7B-Instruct-1M: At least 120GB VRAM (total across GPUs).
- Qwen2.5-14B-Instruct-1M: At least 320GB VRAM (total across GPUs).
If your GPUs do not have sufficient VRAM, you can still use Qwen2.5-1M models for shorter tasks.
Qwen recommend using their custom fork of vLLM to serve the models:
You can also use the previous framework that supports Qwen2.5 for inference, but accuracy degradation may occur for sequences exceeding 262,144 tokens.
GGUF quantized versions of the models are already starting to show up. LM Studio's "official model curator" Bartowski published lmstudio-community/Qwen2.5-7B-Instruct-1M-GGUF and lmstudio-community/Qwen2.5-14B-Instruct-1M-GGUF - sizes range from 4.09GB to 8.1GB for the 7B model and 7.92GB to 15.7GB for the 14B.
These might not work well yet with the full context lengths as the underlying llama.cpp library may need some changes.
I tried running the 8.1GB 7B model using Ollama on my Mac like this:
ollama run hf.co/lmstudio-community/Qwen2.5-7B-Instruct-1M-GGUF:Q8_0
Then with LLM:
llm install llm-ollama
llm models -q qwen # To search for the model ID
# I set a shorter q1m alias:
llm aliases set q1m hf.co/lmstudio-community/Qwen2.5-7B-Instruct-1M-GGUF:Q8_0
I tried piping a large prompt in using files-to-prompt like this:
files-to-prompt ~/Dropbox/Development/llm -e py -c | llm -m q1m 'describe this codebase in detail'
That should give me every Python file in my llm project. Piping that through ttok first told me this was 63,014 OpenAI tokens, I expect that count is similar for Qwen.
The result was disappointing: it appeared to describe just the last Python file that stream. Then I noticed the token usage report:
2,048 input, 999 output
This suggests to me that something's not working right here - maybe the Ollama hosting framework is truncating the input, or maybe there's a problem with the GGUF I'm using?
I'll update this post when I figure out how to run longer prompts through the new Qwen model using GGUF weights on a Mac.
Update: It turns out Ollama has a num_ctx option which defaults to 2048, affecting the input context length. I tried this:
files-to-prompt \
~/Dropbox/Development/llm \
-e py -c | \
llm -m q1m 'describe this codebase in detail' \
-o num_ctx 80000
But I quickly ran out of RAM (I have 64GB but a lot of that was in use already) and hit Ctrl+C to avoid crashing my computer. I need to experiment a bit to figure out how much RAM is used for what context size.
Awni Hannun shared tips for running mlx-community/Qwen2.5-7B-Instruct-1M-4bit using MLX, which should work for up to 250,000 tokens. They ran 120,000 tokens and reported:
- Peak RAM for prompt filling was 22GB
- Peak RAM for generation 12GB
- Prompt filling took 350 seconds on an M2 Ultra
- Generation ran at 31 tokens-per-second on M2 Ultra
2024
Things we learned about LLMs in 2024
A lot has happened in the world of Large Language Models over the course of 2024. Here’s a review of things we figured out about the field in the past twelve months, plus my attempt at identifying key themes and pivotal moments.
[... 7,490 words]When we started working on what became NotebookLM in the summer of 2022, we could fit about 1,500 words in the context window. Now we can fit up to 1.5 million words. (And using various other tricks, effectively fit 25 million words.) The emergence of long context models is, I believe, the single most unappreciated AI development of the past two years, at least among the general public. It radically transforms the utility of these models in terms of actual, practical applications.
Long context prompting tips (via) Interesting tips here from Anthropic's documentation about how to best prompt Claude to work with longer documents.
Put longform data at the top: Place your long documents and inputs (~20K+ tokens) near the top of your prompt, above your query, instructions, and examples. This can significantly improve Claude’s performance across all models. Queries at the end can improve response quality by up to 30% in tests, especially with complex, multi-document inputs.
It recommends using not-quite-valid-XML to add those documents to those prompts, and using a prompt that asks Claude to extract direct quotes before replying to help it focus its attention on the most relevant information:
Find quotes from the patient records and appointment history that are relevant to diagnosing the patient's reported symptoms. Place these in <quotes> tags. Then, based on these quotes, list all information that would help the doctor diagnose the patient's symptoms. Place your diagnostic information in <info> tags.
Context caching for Google Gemini (via) Another new Gemini feature announced today. Long context models enable answering questions against large chunks of text, but the price of those long prompts can be prohibitive - $3.50/million for Gemini Pro 1.5 up to 128,000 tokens and $7/million beyond that.
Context caching offers a price optimization, where the long prefix prompt can be reused between requests, halving the cost per prompt but at an additional cost of $4.50 / 1 million tokens per hour to keep that context cache warm.
Given that hourly extra charge this isn't a default optimization for all cases, but certain high traffic applications might be able to save quite a bit on their longer prompt systems.
It will be interesting to see if other vendors such as OpenAI and Anthropic offer a similar optimization in the future.
Update 14th August 2024: Anthropic's Claude now has its own version of prompt caching.
llm-gemini 0.1a1. I upgraded my llm-gemini plugin to add support for the new Google Gemini Pro 1.5 model, which is beginning to roll out in early access.
The 1.5 model supports 1,048,576 input tokens and generates up to 8,192 output tokens—a big step up from Gemini 1.0 Pro which handled 30,720 and 2,048 respectively.
The big missing feature from my LLM tool at the moment is image input—a fantastic way to take advantage of that huge context window. I have a branch for this which I really need to get into a useful state.
The killer app of Gemini Pro 1.5 is video
Last week Google introduced Gemini Pro 1.5, an enormous upgrade to their Gemini series of AI models.
[... 2,839 words]Our next-generation model: Gemini 1.5 (via) The big news here is about context length: Gemini 1.5 (a Mixture-of-Experts model) will do 128,000 tokens in general release, available in limited preview with a 1 million token context and has shown promising research results with 10 million tokens!
1 million tokens is 700,000 words or around 7 novels—also described in the blog post as an hour of video or 11 hours of audio.
2023
Long context prompting for Claude 2.1. Claude 2.1 has a 200,000 token context, enough for around 500 pages of text. Convincing it to answer a question based on a single sentence buried deep within that content can be difficult, but Anthropic found that adding “Assistant: Here is the most relevant sentence in the context:” to the end of the prompt was enough to raise Claude 2.1’s score from 27% to 98% on their evaluation.