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Secure AI virtual data roomsBMOJOF-primed-HQwen3-8B-Instruct
BMOJOF-primed-HQwen3-8B-Instruct is an 8B parameter instruction-tuned language model that replaces half its standard attention layers with B'MOJO-F hybrid layers (combining sliding-window attention with state-space models). It achieves up to 1.75× faster inference on long contexts (up to 128K tokens) while maintaining performance within ~1–3 points of the base Qwen3-8B model on standard benchmarks. Licensed under Apache 2.0, ungated, and developed by Amazon.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | amazon |
| Parameters | 9.3B |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 69.4k |
| Likes | 2 |
| Last updated | 2026-04-03 |
| Source | amazon/BMOJOF-primed-HQwen3-8B-Instruct |
What BMOJOF-primed-HQwen3-8B-Instruct is
Hybrid Attention + SSM architecture with 36 layers (18 standard Attention + 18 B'MOJO-F). Each B'MOJO-F layer couples sliding-window attention (2048-token window) with a GKA-based state-space model within a single layer, replacing the growing KV cache with fixed-size recurrent state. Precision: bfloat16. Native context: 128K tokens. Derived from Qwen3-8B via Amazon's Hybrid Model Factory priming pipeline. Achieves ~1.5–2× inference speedup at long contexts versus base transformer, with ~2× concurrent sequence capacity before memory saturation.
Run BMOJOF-primed-HQwen3-8B-Instruct locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="amazon/BMOJOF-primed-HQwen3-8B-Instruct")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
ESTIMATE: ~16–24 GB VRAM for inference in bfloat16 precision on single GPU (e.g., A100 40GB, L40S 48GB). Batching and long-context (128K) workloads may require larger allocators. ~2× lower KV cache memory than base Qwen3-8B at equivalent batch size due to hybrid layers. Exact requirements depend on serving framework (vLLM, TGI) and batch size.
Model is instruction-tuned; further fine-tuning is feasible but not documented in card. LoRA/QLoRA adaptation likely compatible with HuggingFace ecosystem (safetensors format), though no explicit fine-tuning examples or guidance provided. Hybrid layer structure (half B'MOJO-F) may require custom LoRA adapter logic; verify framework support before production fine-tuning.
When to avoid it — and what to weigh
- Chain-of-thought reasoning critical — Model card explicitly states this is NOT a thinking model; it does not natively produce chain-of-thought tokens. Avoid if internal reasoning transparency is required.
- Exact quality parity required — Hybrid models show ~1–3 point gaps versus base Qwen3-8B (Long) on short-context benchmarks. If marginal performance loss is unacceptable, use the base transformer instead.
- Cutting-edge SOTA reasoning/math tasks — Benchmarks show gaps in math (65.69 vs 64.56 baseline) and reasoning (76.42 vs 76.27). Specialized reasoning models may outperform on frontier tasks.
- Production use without kernel optimization — B'MOJO-F layers use unfused SSM+sliding-window-attention kernels, slower than optimized alternatives (GKA, GDN). Inference speed advantage requires low-latency serving infrastructure.
License & commercial use
Apache License 2.0 (OSI-approved permissive license). Allows commercial use, modification, and distribution with proper attribution and liability disclaimer.
Apache 2.0 is a clear permissive OSI license. Commercial use, including incorporation into proprietary applications and SaaS offerings, is permitted provided the license and copyright notice are retained and any claims are disclaimed. No commercial use restrictions apply.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Moderate |
| Documentation | Strong |
| License clarity | Clear |
| Deployment complexity | Moderate |
| DEV.co fit | Strong |
| Assessment confidence | High |
Standard LLM security practices apply: prompt injection, jailbreaking, and adversarial input handling are not addressed in card. No stated vulnerability disclosure policy or security audit mentioned. Recommend standard ML security review (input validation, output filtering, rate limiting) before production deployment. Hybrid SSM component is less widely battle-tested than pure transformers; risk profile unknown.
Alternatives to consider
Qwen3-8B (base, non-hybrid)
Drop-in replacement if performance parity is critical and inference speed advantage is not essential. Simpler architecture, mature tooling, no kernel fusion dependency.
GKA-primed-HQwen3-8B-Instruct or Mamba2-primed-HQwen3-8B-Instruct
Alternative hybrid architectures from same Amazon Hybrid Model Factory. GKA ranks higher on benchmarks (~2 pt gap vs 1 pt); Mamba2 is simpler but slower. Choose based on throughput vs. quality tradeoff.
Llama 3.1 8B or Mistral 8B
Mature, widely deployed 8B alternatives. Trade long-context support (128K) for broader ecosystem maturity and community support; no performance advantage on short-context tasks.
Ship BMOJOF-primed-HQwen3-8B-Instruct with senior software developers
Ideal for long-document processing, high-throughput inference, and private LLM deployments. Compare performance and cost on your workload. Review GitHub (awslabs/hybrid-model-factory) and benchmark details. Contact your DevCo architect to test on your infrastructure.
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BMOJOF-primed-HQwen3-8B-Instruct FAQ
Can I use this model commercially?
What GPU do I need for inference?
How does this compare to the base Qwen3-8B?
Can I fine-tune this model?
Build it with a software development company
DEV.co is a software development company delivering production systems to teams building on open source. Our engineers design, integrate, and ship across web, APIs, AI, data, and cloud. If BMOJOF-primed-HQwen3-8B-Instruct is part of your open-source llms roadmap, we can implement, customize, migrate, and maintain it.
Evaluate BMOJOF-primed-HQwen3-8B-Instruct for Your Use Case
Ideal for long-document processing, high-throughput inference, and private LLM deployments. Compare performance and cost on your workload. Review GitHub (awslabs/hybrid-model-factory) and benchmark details. Contact your DevCo architect to test on your infrastructure.