Instructions to use DuoNeural/InternLM3-8B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DuoNeural/InternLM3-8B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DuoNeural/InternLM3-8B-Instruct-GGUF",
	filename="InternLM3-8B-Instruct-IQ1_S.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use DuoNeural/InternLM3-8B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DuoNeural/InternLM3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DuoNeural/InternLM3-8B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DuoNeural/InternLM3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DuoNeural/InternLM3-8B-Instruct-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf DuoNeural/InternLM3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DuoNeural/InternLM3-8B-Instruct-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf DuoNeural/InternLM3-8B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DuoNeural/InternLM3-8B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DuoNeural/InternLM3-8B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DuoNeural/InternLM3-8B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DuoNeural/InternLM3-8B-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DuoNeural/InternLM3-8B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DuoNeural/InternLM3-8B-Instruct-GGUF:Q4_K_M

Ollama
How to use DuoNeural/InternLM3-8B-Instruct-GGUF with Ollama:
```
ollama run hf.co/DuoNeural/InternLM3-8B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use DuoNeural/InternLM3-8B-Instruct-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DuoNeural/InternLM3-8B-Instruct-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for DuoNeural/InternLM3-8B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DuoNeural/InternLM3-8B-Instruct-GGUF to start chatting

Docker Model Runner
How to use DuoNeural/InternLM3-8B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/DuoNeural/InternLM3-8B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use DuoNeural/InternLM3-8B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DuoNeural/InternLM3-8B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.InternLM3-8B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

InternLM3-8B-Instruct-GGUF / README.md

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	---
	license: apache-2.0
	base_model: internlm/internlm3-8b-instruct
	tags:
	- gguf
	- quantized
	- internlm
	- text-generation
	- long-context
	language:
	- en
	- zh
	pipeline_tag: text-generation
	---

	# InternLM3-8B-Instruct — GGUF Quants

	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.

	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](https://huggingface.co/DuoNeural) \|
	\| Website \| [duoneural.com](https://duoneural.com) \|
	\| GitHub \| [github.com/DuoNeural](https://github.com/DuoNeural) \|
	\| X / Twitter \| [@DuoNeural](https://x.com/DuoNeural) \|
	\| Email \| duoneural@proton.me \|
	\| Newsletter \| [duoneural.beehiiv.com](https://duoneural.beehiiv.com) \|
	\| Support \| [buymeacoffee.com/duoneural](https://buymeacoffee.com/duoneural) \|

	### DuoNeural Research Publications

	\| Title \| DOI \|
	\|-------\|-----\|
	\| [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) \|
	\| [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) \|
	\| [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) \|
	\| [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) \|

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

	---
	license: apache-2.0
	base_model: internlm/internlm3-8b-instruct
	tags:
	- gguf
	- quantized
	- internlm
	- text-generation
	- long-context
	language:
	- en
	- zh
	pipeline_tag: text-generation
	---

	# InternLM3-8B-Instruct — GGUF Quants

	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.

	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](https://huggingface.co/DuoNeural) \|
	\| Website \| [duoneural.com](https://duoneural.com) \|
	\| GitHub \| [github.com/DuoNeural](https://github.com/DuoNeural) \|
	\| X / Twitter \| [@DuoNeural](https://x.com/DuoNeural) \|
	\| Email \| duoneural@proton.me \|
	\| Newsletter \| [duoneural.beehiiv.com](https://duoneural.beehiiv.com) \|
	\| Support \| [buymeacoffee.com/duoneural](https://buymeacoffee.com/duoneural) \|

	### DuoNeural Research Publications

	\| Title \| DOI \|
	\|-------\|-----\|
	\| [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) \|
	\| [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) \|
	\| [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) \|
	\| [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) \|

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