Triangle104/Human-Like-Mistral-Nemo-Instruct-2407-Q4_K_S-GGUF

This model was converted to GGUF format from HumanLLMs/Human-Like-Mistral-Nemo-Instruct-2407 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

This model is a fine-tuned version of mistralai/Mistral-Nemo-Instruct-2407, specifically optimized to generate more human-like and conversational responses.

The fine-tuning process employed both Low-Rank Adaptation (LoRA) and Direct Preference Optimization (DPO) to enhance natural language understanding, conversational coherence, and emotional intelligence in interactions.

The proccess of creating this models is detailed in the research paper “Enhancing Human-Like Responses in Large Language Models”.

🛠️ Training Configuration

Base Model: Mistral-Nemo-Instruct-2407
Framework: Axolotl v0.4.1
Hardware: 2x NVIDIA A100 (80 GB) GPUs
Training Time: ~3 hours 40 minutes
Dataset: Synthetic dataset with ≈11,000 samples across 256 diverse topics

💬 Prompt Template

You can use Mistral-Nemo prompt template while using the model:

Mistral-Nemo

[INST] Hello, how are you? [/INST]I'm doing great. How can I help you today? [INST] I'd like to show off how chat templating works! [/INST]

This prompt template is available as a chat template, which means you can format messages using the tokenizer.apply_chat_template() method:

messages = [ {"role": "system", "content": "You are helpful AI asistant."}, {"role": "user", "content": "Hello!"} ] gen_input = tokenizer.apply_chat_template(message, return_tensors="pt") model.generate(**gen_input)

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/Human-Like-Mistral-Nemo-Instruct-2407-Q4_K_S-GGUF --hf-file human-like-mistral-nemo-instruct-2407-q4_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/Human-Like-Mistral-Nemo-Instruct-2407-Q4_K_S-GGUF --hf-file human-like-mistral-nemo-instruct-2407-q4_k_s.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/Human-Like-Mistral-Nemo-Instruct-2407-Q4_K_S-GGUF --hf-file human-like-mistral-nemo-instruct-2407-q4_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/Human-Like-Mistral-Nemo-Instruct-2407-Q4_K_S-GGUF --hf-file human-like-mistral-nemo-instruct-2407-q4_k_s.gguf -c 2048