Slipstream-Max/Emollm-InternLM2.5-7B-chat-GGUF-fp16

Model Details

Model Description

• Developed by: AITA
• Model type: Full-Precision Text Generation LLM (FP16 GGUF format)
• Original Model: https://modelscope.cn/models/chg0901/EmoLLMV3.0/summary
• Precision: FP16 (non-quantized full-precision version)

Repository

• GGUF Converter: llama.cpp
• Model Hub: https://huggingface.co/Slipstream-Max/Emollm-InternLM2.5-7B-chat-GGUF-fp16/

Usage

Method 1: llama.cpp Backend Server + Chatbox

Step 1: Start .llama.cpp Server

./server \
  -m model-fp16.gguf \
  -c 2048 \          # Context length
  --host 0.0.0.0 \   # Allow remote connections
  --port 8080 \      # Server port
  --n-gpu-layers 35  # GPU acceleration (if available)

Step 2: Connect via Chatbox

1. Download Chatbox
2. Configure API endpoint:

   API URL: http://localhost:8080
   Model: (leave empty)
   API Type: llama.cpp
   

3. Set generation parameters:

   {
     "temperature": 0.7,
     "max_tokens": 512,
     "top_p": 0.9
   }
   

Method 2: LM Studio

1. Download LM Studio
2. Load GGUF file:
   • Launch LM Studio
   • Click "Search" -> Select GGUF format
   • Navigate to your model file
3. Configure settings:

   Context Length: 2048
   GPU Offload: Recommended (enable if available)
   Batch Size: 512
   

4. Start chatting through the built-in UI

Precision Details

Filename	Precision	Size	Characteristics
emollmv3.gguf	FP16	[15.5GB]	Full original model precision

Hardware Requirements

Minimum:

• 16GB RAM (for 14B model)
• CPU with AVX/AVX2 instruction set support

Recommended:

• 20GB+ RAM
• CUDA-capable GPU (for acceleration)
• Fast SSD storage (due to large model size)

Key Notes

1. Requires latest llama.cpp (v3+ recommended)
2. Use --n-gpu-layers 35 for GPU acceleration (requires CUDA-enabled build)
3. Initial loading takes longer (2-5 minutes)
4. Requires more memory/storage than quantized versions
5. Use --mlock to prevent swapping

Advantages

• Preserves original model precision
• Ideal for precision-sensitive applications
• No quantization loss
• Suitable for continued fine-tuning

Ethical Considerations

All open-source code and models in this repository are licensed under the MIT License. As the currently open-sourced EmoLLM model may have certain limitations, we hereby state the following:

EmoLLM is currently only capable of providing emotional support and related advisory services, and cannot yet offer professional psychological counseling or psychotherapy services. EmoLLM is not a substitute for qualified mental health professionals or psychotherapists, and may exhibit inherent limitations while potentially generating erroneous, harmful, offensive, or otherwise undesirable outputs. In critical or high-risk scenarios, users must exercise prudence and refrain from treating EmoLLM's outputs as definitive decision-making references, to avoid personal harm, property loss, or other significant damages.

Under no circumstances shall the authors, contributors, or copyright holders be liable for any claims, damages, or other liabilities (whether in contract, tort, or otherwise) arising from the use of or transactions related to the EmoLLM software.

By using EmoLLM, you agree to the above terms and conditions, acknowledge awareness of its potential risks, and further agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities resulting from your use of EmoLLM.

Citation

@misc{2024EmoLLM,
    title={EmoLLM: Reinventing Mental Health Support with Large Language Models},
    author={EmoLLM Team},
    howpublished={\url{https://github.com/SmartFlowAI/EmoLLM}},
    year={2024}
}