gemma-3-4b-coralmc-7000it-v05

gemma-3-4b-coralmc-7000it-v05 : GGUF

This model was finetuned and converted to GGUF format using Unsloth.

Example usage:

• For text only LLMs: ./llama.cpp/llama-cli -hf AfkaraLP/gemma-3-4b-coralmc-7000it-v05 --jinja
• For multimodal models: ./llama.cpp/llama-mtmd-cli -hf AfkaraLP/gemma-3-4b-coralmc-7000it-v05 --jinja

CoralMC Chat Model Prompting Guide

This model was trained on real chat logs from a multiplayer server.
Its training data uses structured conversation windows and predicts either:

1. Chat continuation
2. A response as a specific user

If you prompt it incorrectly, outputs will degrade.

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Message Format

Messages in training look like:

[username]: message text

Multiple messages are joined with newlines to form chat history.

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Task Type 1: Chat Continuation

System Prompt Structure

[UserA]: message
[UserB]: message
...
[UserN]: message

above is the history of coralmc chat
you will now see a message by NextUser
based on that write how the chat might continue

User Prompt

message from NextUser

Assistant Output

Model outputs several following chat lines:

[UserX]: reply
[UserY]: reply
...

When to use

Use this when you want the model to simulate how the chat continues naturally.

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Task Type 2: Respond as Specific User

System Prompt Structure

[UserA]: message
[UserB]: message
...
[UserN]: message

above is the history of coralmc chat
you will now see a message by NextUser
respond to it as TargetUser

User Prompt

message from NextUser

Assistant Output

single reply message written as TargetUser

When to use

Use this when you want the model to answer in-character as a specific username.

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Style Expectations

• Short informal chat lines
• Multiplayer server tone
• No narration
• No emojis unless chat history already uses them
• Usernames preserved in brackets

---

Example: Continuation

System

[Alex]: anyone online?
[Bea]: yeah what's up
[Alex]: need help building spawn

above is the history of coralmc chat
you will now see a message by Kai
based on that write how the chat might continue

User

I can help if you have materials

Assistant

[Alex]: nice meet at spawn
[Bea]: I’ll grab wood
[Kai]: cool coming now

---

Example: Respond as Specific User

System

[Alex]: anyone online?
[Bea]: yeah what's up

above is the history of coralmc chat
you will now see a message by Kai
respond to it as Alex

User

server lagging again

Assistant

ugh yeah TPS is dying

---

Practical Tips

• Always include recent chat history
• Always include the system instruction text
• Keep usernames consistent
• Do not add extra roles or narration
• Do not remove the bracket format

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Intended Use

• Server chat simulation
• Roleplay continuation
• Dataset augmentation
• Bot personality testing

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Final Note

This model predicts chat patterns.
It is not trained for complex reasoning outside chat simulation.

Available Model files:

• gemma-3-4b-it.Q8_0.gguf
• gemma-3-4b-it.BF16-mmproj.gguf

⚠️ Ollama Note for Vision Models

Important: Ollama currently does not support separate mmproj files for vision models.

To create an Ollama model from this vision model:

1. Place the Modelfile in the same directory as the finetuned bf16 merged model
2. Run: ollama create model_name -f ./Modelfile (Replace model_name with your desired name)

This will create a unified bf16 model that Ollama can use.

Note

The model's BOS token behavior was adjusted for GGUF compatibility. This was trained 2x faster with Unsloth <img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>