Instructions to use jjee2/lora_recycle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jjee2/lora_recycle with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="jjee2/lora_recycle",
	filename="Aratron1811__llama-3.1-8B-Instruct-abliterated-comrade/Meta-Llama-3.1-8B-Instruct-abliterated-TQ2_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use jjee2/lora_recycle with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jjee2/lora_recycle:TQ2_0
# Run inference directly in the terminal:
llama-cli -hf jjee2/lora_recycle:TQ2_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf jjee2/lora_recycle:TQ2_0
# Run inference directly in the terminal:
llama-cli -hf jjee2/lora_recycle:TQ2_0

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf jjee2/lora_recycle:TQ2_0
# Run inference directly in the terminal:
./llama-cli -hf jjee2/lora_recycle:TQ2_0

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf jjee2/lora_recycle:TQ2_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf jjee2/lora_recycle:TQ2_0

Use Docker

docker model run hf.co/jjee2/lora_recycle:TQ2_0

LM Studio
Jan
Ollama
How to use jjee2/lora_recycle with Ollama:
```
ollama run hf.co/jjee2/lora_recycle:TQ2_0
```

Unsloth Studio

How to use jjee2/lora_recycle with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jjee2/lora_recycle to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for jjee2/lora_recycle to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for jjee2/lora_recycle to start chatting

Docker Model Runner
How to use jjee2/lora_recycle with Docker Model Runner:
```
docker model run hf.co/jjee2/lora_recycle:TQ2_0
```

Lemonade

How to use jjee2/lora_recycle with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull jjee2/lora_recycle:TQ2_0

Run and chat with the model

lemonade run user.lora_recycle-TQ2_0

List all available models

lemonade list

jjee2 commited on Apr 14

Commit

ec96bb3

verified ·

1 Parent(s): 78abcf8

Add zjudai__flowertune-general-nlp-lora-llama-3.1-8b-instruct

Browse files

Files changed (4) hide show

zjudai__flowertune-general-nlp-lora-llama-3.1-8b-instruct/.gitattributes +35 -0
zjudai__flowertune-general-nlp-lora-llama-3.1-8b-instruct/README.md +26 -0
zjudai__flowertune-general-nlp-lora-llama-3.1-8b-instruct/adapter_config.json +32 -0
zjudai__flowertune-general-nlp-lora-llama-3.1-8b-instruct/adapter_model.safetensors +3 -0

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zjudai__flowertune-general-nlp-lora-llama-3.1-8b-instruct/README.md ADDED Viewed

	@@ -0,0 +1,26 @@

+---
+base_model: meta-llama/Llama-3.1-8B-Instruct
+tags:
+- peft
+- lora
+- federated-learning
+- flower
+datasets:
+- vicgalle/alpaca-gpt4
+---
+# FlowerTune LoRA Model
+This is a LoRA adapter for meta-llama/Llama-3.1-8B-Instruct fine-tuned with Flower federated learning framework on a general NLP dataset.
+## Training Details
+- Dataset: vicgalle/alpaca-gpt4
+- Training method: Federated LoRA fine-tuning with FlowerTune
+- Framework: Flower
+This model is a LoRA adapter fine-tuned on meta-llama/Llama-3.1-8B-Instruct using the Flower federated learning framework. It was trained on a general NLP dataset (vicgalle/alpaca-gpt4) through distributed learning to improve performance.
+## Links
+- FlowerTune Homepage: [https://huggingface.co/zjudai/FlowerTune](https://huggingface.co/zjudai/FlowerTune)
+- FlowerTune Collection: [https://huggingface.co/collections/zjudai/flowertune-lora-collection-67ecd5d0dae6145cbf798439](https://huggingface.co/collections/zjudai/flowertune-lora-collection-67ecd5d0dae6145cbf798439)

zjudai__flowertune-general-nlp-lora-llama-3.1-8b-instruct/adapter_config.json ADDED Viewed

	@@ -0,0 +1,32 @@

+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
+  "bias": "none",
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 64,
+  "lora_bias": false,
+  "lora_dropout": 0.075,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 32,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "q_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

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