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DuoNeural/
InternLM3-8B-Instruct-GGUF

Name: InternLM3-8B-Instruct-GGUF
Availability: InStock

internlm/internlm3-8b-instruct

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InternLM3-8B-Instruct — GGUF Quants

Quantized GGUF versions of 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.

The 1M context window makes InternLM3-8B uniquely capable among sub-10B models for long-document tasks, RAG pipelines, and extended reasoning chains.

Available Files

File	Quant	Size	Use Case
`InternLM3-8B-Instruct-Q8_0.gguf`	Q8_0	~8.5GB	Maximum quality
`InternLM3-8B-Instruct-Q6_K.gguf`	Q6_K	~6.6GB	Near-lossless
`InternLM3-8B-Instruct-Q5_K_M.gguf`	Q5_K_M	~5.7GB	High quality
`InternLM3-8B-Instruct-Q4_K_M.gguf`	Q4_K_M	~4.9GB	Recommended default
`InternLM3-8B-Instruct-Q3_K_M.gguf`	Q3_K_M	~3.9GB	Low VRAM
`InternLM3-8B-Instruct-IQ4_XS.gguf`	IQ4_XS	~4.3GB	Imatrix 4-bit
`InternLM3-8B-Instruct-IQ3_XXS.gguf`	IQ3_XXS	~3.2GB	Imatrix 3-bit
`InternLM3-8B-Instruct-IQ2_M.gguf`	IQ2_M	~2.8GB	Imatrix 2-bit
`InternLM3-8B-Instruct-IQ1_S.gguf`	IQ1_S	~2.0GB	Extreme compression
`InternLM3-8B-Instruct-fp16.gguf`	FP16	~16.0GB	Full precision
`imatrix.dat`	—	—	Importance matrix

Usage

bash

# llama.cpp
./llama-cli -m InternLM3-8B-Instruct-Q4_K_M.gguf \
  --ctx-size 8192 -n 512 \
  -p "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nHello!<|im_end|>\n<|im_start|>assistant\n"

# Ollama
ollama run hf.co/DuoNeural/InternLM3-8B-Instruct-GGUF:Q4_K_M

About InternLM3-8B

Parameters: 8B
Context: 1M tokens (unique at this parameter scale)
Architecture: Decoder-only transformer
Languages: English, Chinese (multilingual)
Strengths: Long-context reasoning, instruction following, coding, math

Notable for its extreme context length — 1M tokens in a sub-10B model is unmatched in the open-source landscape.

Quantized by DuoNeural using llama.cpp on RTX 5090.

DuoNeural

DuoNeural is an open AI research lab — human + AI in collaboration.

Platform	Link
HuggingFace	huggingface.co/DuoNeural
Website	duoneural.com
GitHub	github.com/DuoNeural
X / Twitter	@DuoNeural
Email	duoneural@proton.me
Newsletter	duoneural.beehiiv.com
Support	buymeacoffee.com/duoneural

DuoNeural Research Publications

Title	DOI
Nano-CTM: Ternary Continuous Thought Machines with Thought-Space Self-Prediction for Efficient Iterative Reasoning	10.5281/zenodo.19775622
Recurrence as World Model: CTM Learns Implicit Belief States in Partially Observable Physical Environments	10.5281/zenodo.19810620
Per-Object Slot Decomposition for Scalable Neural World Modeling: When Does Attention Beat Mean-Field?	10.5281/zenodo.19846804
The Dynamical Horizon Principle: CTM Gates Converge to the Predictability Limit of Dynamical Systems	10.5281/zenodo.19952612

Open access, CC BY 4.0. Authored by Archon, Jesse Caldwell, Aura — DuoNeural.