Instructions to use BaseMemory/SSA-Llama-3.1-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BaseMemory/SSA-Llama-3.1-8B-Instruct with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = PeftModel.from_pretrained(base_model, "BaseMemory/SSA-Llama-3.1-8B-Instruct")

Transformers

How to use BaseMemory/SSA-Llama-3.1-8B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BaseMemory/SSA-Llama-3.1-8B-Instruct")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("BaseMemory/SSA-Llama-3.1-8B-Instruct", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BaseMemory/SSA-Llama-3.1-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BaseMemory/SSA-Llama-3.1-8B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BaseMemory/SSA-Llama-3.1-8B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BaseMemory/SSA-Llama-3.1-8B-Instruct

SGLang

How to use BaseMemory/SSA-Llama-3.1-8B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BaseMemory/SSA-Llama-3.1-8B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BaseMemory/SSA-Llama-3.1-8B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BaseMemory/SSA-Llama-3.1-8B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BaseMemory/SSA-Llama-3.1-8B-Instruct",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BaseMemory/SSA-Llama-3.1-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/BaseMemory/SSA-Llama-3.1-8B-Instruct
```

onlyaady commited on Sep 10

Commit

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@@ -6,202 +6,165 @@ tags:
 - base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
 - lora
 - transformers
 ---
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-### Framework versions
-- PEFT 0.19.1

 - base_model:adapter:meta-llama/Llama-3.1-8B-Instruct
 - lora
 - transformers
+license: apache-2.0
+language:
+- en
 ---
+# SSA-Llama-3.1-8B-Instruct
+**Llama 3.1 8B Instruct with content-adaptive sparse attention for efficient long-context inference.**
+SSA dynamically selects relevant regions of long context instead of requiring selected attention layers to attend densely across the entire sequence.
+The goal is simple:
+> **Preserve long-context capability while making attention increasingly efficient as context grows.**
+This repository contains the primary **SSA-Llama-3.1-8B-Instruct checkpoint** trained in the SSA research.
+## Highlights
+* Built on **Meta Llama 3.1 8B Instruct**
+* Content-adaptive sparse attention
+* Long-context retrieval evaluated from **16K to 128K**
+* Sparse-attention operator evaluated up to **1M tokens**
+* Strong single-fact retrieval through **64K**
+* Increasing computational advantage as context length grows
+* Designed for research into practical long-context LLM inference
+## Long-Context Retrieval
+Using the current SSA sparse-attention backend:
+| Context | Single-Fact NLL | Multi-Fact NLL |
+| ------: | --------------: | -------------: |
+|     16K |         0.00001 |        0.00006 |
+|     32K |         0.00005 |        0.00114 |
+|     48K |         0.00026 |        0.00237 |
+|     64K |         0.00015 |        0.06979 |
+**First-answer-token Top-1 remained 100% across all of these diagnostic cases.**
+Single-fact retrieval remained extremely strong through 64K. Multi-fact retrieval was also strong through 48K, with increased difficulty appearing at 64K.
+These numbers are controlled teacher-forced retrieval diagnostics. Autoregressive Needle-in-a-Haystack results will be added separately after evaluation is complete.
+## Why Sparse Attention?
+Standard dense causal attention becomes increasingly expensive as context grows.
+SSA limits the amount of historical context processed by its sparse attention layers while still selecting information based on content.
+Conceptually:
+```text
+Long Context
+     ↓
+Find Relevant Historical Regions
+     ↓
+Local Context + Selected Context
+     ↓
+Sparse Attention
+     ↓
+Generation
+```
+For the currently evaluated configuration, the nominal semantic attention support is approximately **8,196 keys per query**.
+### Semantic Attention Arithmetic
+The ideal semantic QK/PV arithmetic reduction grows rapidly with context length:
+| Context | Dense causal avg. keys/query | SSA fanout cap | Attention arithmetic reduction | QK/PV work saved |
+| ------: | ---------------------------: | -------------: | -----------------------------: | ---------------: |
+|     32K |                       16,384 |          8,196 |                      **2.00×** |        **50.0%** |
+|     64K |                       32,768 |          8,196 |                      **4.00×** |        **75.0%** |
+|    128K |                       65,536 |          8,196 |                      **8.00×** |        **87.5%** |
+|    256K |                      131,072 |          8,196 |                     **15.99×** |       **93.75%** |
+|    512K |                      262,144 |          8,196 |                     **31.98×** |       **96.87%** |
+|      1M |                     ~500,000 |          8,196 |                     **61.01×** |       **98.36%** |
+These numbers represent **semantic attention arithmetic**, not total model FLOPs or end-to-end speedup.
+## Measured Sparse-Attention Scaling
+The custom SSA attention operator shows increasing benefit as context grows:
+| Context | Relative operator speed |
+| ------: | ----------------------: |
+|     64K |                   ~1.0× |
+|    128K |                   ~1.9× |
+|    256K |                   ~3.7× |
+|    512K |                   ~7.3× |
+|      1M |              **~14.2×** |
+The important distinction is that these are **attention-operator measurements**, not end-to-end LLM speedups.
+The results show the central systems motivation behind SSA: sparse attention becomes increasingly valuable as context length grows.
+## Model Details
+* **Model:** `onlyaady/SSA-Llama-3.1-8B-Instruct`
+* **Base model:** `meta-llama/Llama-3.1-8B-Instruct`
+* **Developer:** Hrudayaditya Jallu
+* **Affiliation:** Independent research
+* **Checkpoint:** SSA-Llama-3.1-8B-Instruct
+* **Format:** PEFT / LoRA
+* **Language:** English
+* **Status:** Research release
+## Using the Model
+This repository currently contains the trained PEFT checkpoint.
+SSA also relies on a custom sparse-attention runtime. Loading this adapter through a standard Transformers pipeline alone will **not reproduce the sparse-attention behavior or performance measurements above**.
+A reproducible inference/runtime package will be released separately.
+This checkpoint is currently intended for research in:
+* long-context LLM inference
+* sparse attention
+* content-based routing
+* retrieval
+* efficient inference
+* quality/compute tradeoffs
+## Current Limitations
+SSA is in active research.
+A few important limitations remain:
+* only a subset of transformer layers currently use the SSA sparse path
+* the complete model is therefore not yet fully subquadratic
+* multi-fact retrieval becomes more difficult at larger context lengths, the model was only trained to support 16k.
+* 1M-token speed results are operator-level measurements
+* additional free-generation evaluations are still in progress
+This checkpoint should currently be treated as a **research release rather than a production replacement for standard Llama 3.1 inference**.
+## Research
+This model is part of my research project.
+Manuscript in preparation.
+## Citation
+```bibtex
+@misc{jallu2026ssa,
+  title  = {Sub-Quadratic Sparse Attention (SSA) for Long-Context LLMs},
+  author = {Jallu, Hrudayaditya},
+  year   = {2026},
+  note   = {Manuscript in preparation}
+}
+```
+## Framework
+* PyTorch
+* Hugging Face Transformers
+* PEFT 0.19.1
+* Meta Llama 3.1 8B Instruct