internlm/internlm3-8b-instruct-gguf

InternLM3-8B-Instruct GGUF Model

Introduction

The internlm3-8b-instruct model in GGUF format can be utilized by llama.cpp, a highly popular open-source framework for Large Language Model (LLM) inference, across a variety of hardware platforms, both locally and in the cloud. This repository offers internlm3-8b-instruct models in GGUF format in both half precision and various low-bit quantized versions, including q5_0, q5_k_m, q6_k, and q8_0.

In the subsequent sections, we will first present the installation procedure, followed by an explanation of the model download process. And finally we will illustrate the methods for model inference and service deployment through specific examples.

Installation

We recommend building llama.cpp from source. The following code snippet provides an example for the Linux CUDA platform. For instructions on other platforms, please refer to the official guide.

• Step 1: create a conda environment and install cmake

conda create --name internlm3 python=3.10 -y
conda activate internlm3
pip install cmake

• Step 2: clone the source code and build the project

git clone --depth=1 https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release -j

All the built targets can be found in the sub directory build/bin

In the following sections, we assume that the working directory is at the root directory of llama.cpp.

Download models

In the introduction section, we mentioned that this repository includes several models with varying levels of computational precision. You can download the appropriate model based on your requirements. For instance, internlm3-8b-instruct-fp16.gguf can be downloaded as below：

pip install huggingface-hub
huggingface-cli download internlm/internlm3-8b-instruct-gguf internlm3-8b-instruct.gguf --local-dir . --local-dir-use-symlinks False

Inference

You can use llama-cli for conducting inference. For a detailed explanation of llama-cli, please refer to this guide

chat example

Here is an example of using the thinking system prompt.

thinking_system_prompt="<|im_start|>system\nYou are an expert mathematician with extensive experience in mathematical competitions. You approach problems through systematic thinking and rigorous reasoning. When solving problems, follow these thought processes:\n## Deep Understanding\nTake time to fully comprehend the problem before attempting a solution. Consider:\n- What is the real question being asked?\n- What are the given conditions and what do they tell us?\n- Are there any special restrictions or assumptions?\n- Which information is crucial and which is supplementary?\n## Multi-angle Analysis\nBefore solving, conduct thorough analysis:\n- What mathematical concepts and properties are involved?\n- Can you recall similar classic problems or solution methods?\n- Would diagrams or tables help visualize the problem?\n- Are there special cases that need separate consideration?\n## Systematic Thinking\nPlan your solution path:\n- Propose multiple possible approaches\n- Analyze the feasibility and merits of each method\n- Choose the most appropriate method and explain why\n- Break complex problems into smaller, manageable steps\n## Rigorous Proof\nDuring the solution process:\n- Provide solid justification for each step\n- Include detailed proofs for key conclusions\n- Pay attention to logical connections\n- Be vigilant about potential oversights\n## Repeated Verification\nAfter completing your solution:\n- Verify your results satisfy all conditions\n- Check for overlooked special cases\n- Consider if the solution can be optimized or simplified\n- Review your reasoning process\nRemember:\n1. Take time to think thoroughly rather than rushing to an answer\n2. Rigorously prove each key conclusion\n3. Keep an open mind and try different approaches\n4. Summarize valuable problem-solving methods\n5. Maintain healthy skepticism and verify multiple times\nYour response should reflect deep mathematical understanding and precise logical thinking, making your solution path and reasoning clear to others.\nWhen you're ready, present your complete solution with:\n- Clear problem understanding\n- Detailed solution process\n- Key insights\n- Thorough verification\nFocus on clear, logical progression of ideas and thorough explanation of your mathematical reasoning. Provide answers in the same language as the user asking the question, repeat the final answer using a '\\boxed{}' without any units, you have [[8192]] tokens to complete the answer.\n<|im_end|>\n"

build/bin/llama-cli \
    --model internlm3-8b-instruct.gguf  \
    --predict 2048 \
    --ctx-size 8192 \
    --gpu-layers 48 \
    --temp 0.8 \
    --top-p 0.8 \
    --top-k 50 \
    --seed 1024 \
    --color \
    --prompt "$thinking_system_prompt" \
    --interactive \
    --multiline-input \
    --conversation \
    --verbose \
    --logdir workdir/logdir \
    --in-prefix "<|im_start|>user\n" \
    --in-suffix "<|im_end|>\n<|im_start|>assistant\n"

Then input your question like Given the function\(f(x)=\mathrm{e}^{x}-ax - a^{3}\),\n(1) When \(a = 1\), find the equation of the tangent line to the curve \(y = f(x)\) at the point \((1,f(1))\).\n(2) If \(f(x)\) has a local minimum and the minimum value is less than \(0\), determine the range of values for \(a\)..

Function call example

llama-cli example:

build/bin/llama-cli \
    --model internlm3-8b-instruct.gguf \
    --predict 512 \
    --ctx-size 4096 \
    --gpu-layers 48 \
    --temp 0.8 \
    --top-p 0.8 \
    --top-k 50 \
    --seed 1024 \
    --color \
    --prompt '<|im_start|>system\nYou are InternLM-Chat, a harmless AI assistant.<|im_end|>\n<|im_start|>system name=<|plugin|>[{"name": "get_current_weather", "parameters": {"required": ["location"], "type": "object", "properties": {"location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"}, "unit": {"type": "string"}}}, "description": "Get the current weather in a given location"}]<|im_end|>\n<|im_start|>user\n' \
    --interactive \
    --multiline-input \
    --conversation \
    --verbose \
    --in-suffix "<|im_end|>\n<|im_start|>assistant\n" \
    --special

Conversation results:

<s><|im_start|>system
You are InternLM-Chat, a harmless AI assistant.<|im_end|>
<|im_start|>system name=<|plugin|>[{"name": "get_current_weather", "parameters": {"required": ["location"], "type": "object", "properties": {"location": {"type": "string", "description": "The city and state, e.g. San Francisco, CA"}, "unit": {"type": "string"}}}, "description": "Get the current weather in a given location"}]<|im_end|>
<|im_start|>user

> I want to know today's weather in Shanghai
I need to use the get_current_weather function to get the current weather in Shanghai.<|action_start|><|plugin|>
{"name": "get_current_weather", "parameters": {"location": "Shanghai"}}<|action_end|>32
<|im_end|>

> <|im_start|>environment name=<|plugin|>\n{"temperature": 22}
The current temperature in Shanghai is 22 degrees Celsius.<|im_end|>

> 

Serving

llama.cpp provides an OpenAI API compatible server - llama-server. You can deploy internlm3-8b-instruct.gguf into a service like this:

./build/bin/llama-server -m ./internlm3-8b-instruct.gguf -ngl 48

At the client side, you can access the service through OpenAI API:

from openai import OpenAI
client = OpenAI(
    api_key='YOUR_API_KEY',
    base_url='http://localhost:8080/v1'
)
model_name = client.models.list().data[0].id
response = client.chat.completions.create(
  model=model_name,
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": " provide three suggestions about time management"},
  ],
  temperature=0.8,
  top_p=0.8
)
print(response)