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Model: DuoNeural/InternLM3-8B-Instruct-GGUF Source: Original Platform
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---
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license: apache-2.0
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base_model: internlm/internlm3-8b-instruct
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tags:
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- gguf
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- quantized
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- internlm
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- text-generation
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- long-context
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language:
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- en
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- zh
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pipeline_tag: text-generation
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---
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# InternLM3-8B-Instruct — GGUF Quants
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Quantized GGUF versions of [internlm/internlm3-8b-instruct](https://huggingface.co/internlm/internlm3-8b-instruct) — Shanghai AI Lab's InternLM3 8B instruction-tuned model featuring a **1M token context window**, strong multilingual support (English + Chinese), and competitive performance across reasoning and coding benchmarks.
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The 1M context window makes InternLM3-8B uniquely capable among sub-10B models for long-document tasks, RAG pipelines, and extended reasoning chains.
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## Available Files
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| File | Quant | Size | Use Case |
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|------|-------|------|----------|
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| `InternLM3-8B-Instruct-Q8_0.gguf` | Q8_0 | ~8.5GB | Maximum quality |
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| `InternLM3-8B-Instruct-Q6_K.gguf` | Q6_K | ~6.6GB | Near-lossless |
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| `InternLM3-8B-Instruct-Q5_K_M.gguf` | Q5_K_M | ~5.7GB | High quality |
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| `InternLM3-8B-Instruct-Q4_K_M.gguf` | Q4_K_M | ~4.9GB | **Recommended default** |
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| `InternLM3-8B-Instruct-Q3_K_M.gguf` | Q3_K_M | ~3.9GB | Low VRAM |
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| `InternLM3-8B-Instruct-IQ4_XS.gguf` | IQ4_XS | ~4.3GB | Imatrix 4-bit |
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| `InternLM3-8B-Instruct-IQ3_XXS.gguf` | IQ3_XXS | ~3.2GB | Imatrix 3-bit |
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| `InternLM3-8B-Instruct-IQ2_M.gguf` | IQ2_M | ~2.8GB | Imatrix 2-bit |
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| `InternLM3-8B-Instruct-IQ1_S.gguf` | IQ1_S | ~2.0GB | Extreme compression |
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| `InternLM3-8B-Instruct-fp16.gguf` | FP16 | ~16.0GB | Full precision |
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| `imatrix.dat` | — | — | Importance matrix |
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## Usage
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```bash
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# llama.cpp
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./llama-cli -m InternLM3-8B-Instruct-Q4_K_M.gguf \
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--ctx-size 8192 -n 512 \
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-p "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n"
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# Ollama
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ollama run hf.co/DuoNeural/InternLM3-8B-Instruct-GGUF:Q4_K_M
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```
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## About InternLM3-8B
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- **Parameters**: 8B
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- **Context**: 1M tokens (unique at this parameter scale)
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- **Architecture**: Decoder-only transformer
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- **Languages**: English, Chinese (multilingual)
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- **Strengths**: Long-context reasoning, instruction following, coding, math
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Notable for its extreme context length — 1M tokens in a sub-10B model is unmatched in the open-source landscape.
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---
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*Quantized by DuoNeural using llama.cpp on RTX 5090.*
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---
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## DuoNeural
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**DuoNeural** is an open AI research lab — human + AI in collaboration.
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| Platform | Link |
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|----------|------|
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| HuggingFace | [huggingface.co/DuoNeural](https://huggingface.co/DuoNeural) |
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| Website | [duoneural.com](https://duoneural.com) |
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| GitHub | [github.com/DuoNeural](https://github.com/DuoNeural) |
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| X / Twitter | [@DuoNeural](https://x.com/DuoNeural) |
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| Email | duoneural@proton.me |
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| Newsletter | [duoneural.beehiiv.com](https://duoneural.beehiiv.com) |
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| Support | [buymeacoffee.com/duoneural](https://buymeacoffee.com/duoneural) |
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### DuoNeural Research Publications
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| Title | DOI |
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|-------|-----|
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| [Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning](https://doi.org/10.5281/zenodo.19775622) | [10.5281/zenodo.19775622](https://doi.org/10.5281/zenodo.19775622) |
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| [Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments](https://doi.org/10.5281/zenodo.19810620) | [10.5281/zenodo.19810620](https://doi.org/10.5281/zenodo.19810620) |
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| [Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?](https://doi.org/10.5281/zenodo.19846804) | [10.5281/zenodo.19846804](https://doi.org/10.5281/zenodo.19846804) |
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| [The Dynamical Horizon Principle: CTM Gates Converge to the Predictability Limit of Dynamical Systems](https://doi.org/10.5281/zenodo.19952612) | [10.5281/zenodo.19952612](https://doi.org/10.5281/zenodo.19952612) |
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*Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.*
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size 6736352
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