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 Settings

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

DuoNeural commited on about 1 month ago

Commit

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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.*