pipeline_tag, tags, base_model, datasets

pipeline_tag	tags	base_model	datasets
text-generation	gguf llama.cpp unsloth conversational	unsloth/Phi-4-unsloth-bnb-4bit	Mathieu-Thomas-JOSSET/michael_abab_conversations_infini_instruct.jsonl

joke-finetome-model-gguf-phi4-20260112-081758 : GGUF

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

Example usage:

• For text only LLMs: ./llama.cpp/llama-cli -hf Mathieu-Thomas-JOSSET/joke-finetome-model-gguf-phi4-20260112-081758 --jinja
• For multimodal models: ./llama.cpp/llama-mtmd-cli -hf Mathieu-Thomas-JOSSET/joke-finetome-model-gguf-phi4-20260112-081758 --jinja

Available Model files:

• phi-4.Q8_0.gguf

Ollama

An Ollama Modelfile is included for easy deployment. This was trained 2x faster with Unsloth

Training artifacts

• Plot (interactive): reports/training_loss_step.html
• Run manifest: reports/run_manifest.json
• Inference sample: reports/inference_sample.json
• Config snapshot: reports/config_snapshot.json

Inference

This repository contains a GGUF model intended to be used with llama.cpp and/or deployed on Hugging Face Inference Endpoints (llama.cpp container).

Recommended Inference Endpoints knobs:

• Max tokens / request: 1024
• Max concurrent requests: 2

Local llama.cpp (Phi-4 template)

llama-cli -hf Mathieu-Thomas-JOSSET/joke-finetome-model-gguf-phi4-20260112-081758:q8_0 -cnv --chat-template phi4

Hugging Face Inference Endpoint (llama.cpp)

When creating an endpoint, select this repo and the GGUF file <your_model>.gguf (quant: q8_0). Recommended settings are stored in: inference/endpoint_recipe.json.

Python client example: inference/hf_endpoint_client.py