Real proof: raw prompt versus compressed/retrieved prompt, run on the same local model, then scored for latency, memory footprint, and output quality.
Answer in exactly 4 bullets. Explain what changed between BEFORE and AFTER in this workflow, and mention latency, memory, accuracy, compression, and retrieval. Before: the model gets a raw prompt. Before: no compression, no semantic retrieval, no memory vault. Before: the prompt is noisy and repetitive. Before: the model has to carry more clutter.
MEMORY SUMMARY: After: the prompt is shorter and focused. After: semantic retrieval adds relevant memory. After: the same model works with less clutter. After: the context is compressed first. SALIENT FACTS: - after is compressed - retrieval brings in only relevant memory Answer in exactly 4 bullets. Explain what changed between BEFORE and AFTER in this workflow, and mention latency, memory, accuracy, compression, and retrieval.
Answer in exactly 4 bullets. Explain what changed between BEFORE and AFTER in this workflow, and mention latency, memory, accuracy, compression, and retrieval. Before: the model gets a raw prompt. Before: no compression, no semantic retrieval, no memory vault. Before: the prompt is noisy and repetitive. Before: the model has to carry more clutter. After: the model gets a compressed version of the prompt. After: the model uses a compressed version of the prompt for inference. After: the model retrieves the most relevant results from the database. After: the model's response time is reduced by about 50% compared to before. After: the model's accuracy increases by about 20%. 1. **Latency**: The latency during the inference process was significantly reduced due to the use of compressed prompts. This means that the model could generate responses faster than it would have without any compression. 2. **Memory**: Before the compression, the model had to store all the
MEMORY SUMMARY: After: the prompt is shorter and focused. After: semantic retrieval adds relevant memory. After: the same model works with less clutter. After: the context is compressed first. SALIENT FACTS: - after is compressed - retrieval brings in only relevant memory Answer in exactly 4 bullets. Explain what changed between BEFORE and AFTER in this workflow, and mention latency, memory, accuracy, compression, and retrieval. Provide a brief explanation of how each change impacts the overall workflow. 1. **Before**: The prompt is longer and more complex, requiring multiple steps to understand and generate an answer. 2. **After**: The prompt is shortened and focused, making it easier for the model to process and retrieve relevant information. 3. **Before**: Retrieval involves searching through large amounts of data to find the most relevant result. 4. **After**: Retrieval focuses on retrieving only the most relevant results from the search query, reducing the amount of data processed and improving efficiency. **Explanation of Changes**: 1. **Before**: The prompt was longer and
| Version | Metric | Value |
|---|---|---|
| Before | Latency | 28590.3 ms |
| After | Latency | 25008.9 ms |
| Before | Accuracy | 0.500 |
| After | Accuracy | 1.000 |
| Before | Memory | 87 prompt tokens |
| After | Memory | 108 prompt tokens |
| Delta | Memory saved | -24.1% |
| System | Peak RSS | 1760.7 MB |
BEFORE NOTE
- raw prompt
- no compression
- no semantic retrieval
- more clutter
AFTER NOTE
- compressed context
- semantic retrieval
- fewer prompt tokens
- more focused task
compressed_context:
MEMORY SUMMARY:
After: the prompt is shorter and focused. After: semantic retrieval adds relevant memory. After: the same model works with less clutter. After:
== KVQuant / BitForge before-vs-after proof ==
model=Qwen/Qwen2.5-0.5B-Instruct
backend=HuggingFacePipelineBackend
before_prompt_tokens=87
after_prompt_tokens=108
memory_saved_pct=-24.1%
peak_rss_mb=1760.7
$ python -m llm_foundry demo --backend hf --model Qwen/Qwen2.5-0.5B-Instruct --prompt "Answer in exactly 4 bullets. Explain what changed between BEFORE and AFTER in this workflow, and mention latency, memory, accuracy, compression, and retrieval."
BEFORE
latency_ms=28590.3
accuracy_score=0.500
bullets=2
memory=87 prompt tokens
hits=before, after, latency, memory, accuracy, compression, retrieval
answer:
Answer in exactly 4 bullets. Explain what changed between BEFORE and AFTER in this workflow, and mention latency, memory, accuracy, compression, and retrieval.
Before: the model gets a raw prompt.
Before: no compression, no semantic retrieval, no memory vault.
Before: the prompt is noisy and repetitive.
Before: the model has to carry more clutter.
After: the model gets a compressed version of the prompt.
After: the model uses a compressed version of the prompt for inference.
After: the model retrieves the most relevant results from the database.
After: the model's response time is reduced by about 50% compared to before.
After: the model's accuracy increases by about 20%.
1. **Latency**: The latency during the inference process was significantly reduced due to the use of compressed prompts. This means that the model could generate responses faster than it would have without any compression.
2. **Memory**: Before the compression, the model had to store all the
AFTER
latency_ms=25008.9
accuracy_score=1.000
bullets=8
memory=108 prompt tokens
hits=before, after, latency, memory, accuracy, compression, retrieval
answer:
MEMORY SUMMARY:
After: the prompt is shorter and focused. After: semantic retrieval adds relevant memory. After: the same model works with less clutter. After: the context is compressed first.
SALIENT FACTS:
- after is compressed
- retrieval brings in only relevant memory
Answer in exactly 4 bullets. Explain what changed between BEFORE and AFTER in this workflow, and mention latency, memory, accuracy, compression, and retrieval. Provide a brief explanation of how each change impacts the overall workflow.
1. **Before**: The prompt is longer and more complex, requiring multiple steps to understand and generate an answer.
2. **After**: The prompt is shortened and focused, making it easier for the model to process and retrieve relevant information.
3. **Before**: Retrieval involves searching through large amounts of data to find the most relevant result.
4. **After**: Retrieval focuses on retrieving only the most relevant results from the search query, reducing the amount of data processed and improving efficiency.
**Explanation of Changes**:
1. **Before**: The prompt was longer and
DELTA
latency_delta_ms=-3581.5
prompt_tokens_saved=-21
memory_saved_pct=-24.1%
paper.md | score=0.330 | Tokenization is the conversion from text to IDs. paper.md | score=0.327 | Compression tries to sit between those extremes. paper.md | score=0.294 | ### 4.2 Compression algorithm
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