Instructions to use KandarpParmar16/GenerateWorkflowGemmaFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KandarpParmar16/GenerateWorkflowGemmaFT with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/gemma-2-9b-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "KandarpParmar16/GenerateWorkflowGemmaFT")

llama-cpp-python

How to use KandarpParmar16/GenerateWorkflowGemmaFT with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="KandarpParmar16/GenerateWorkflowGemmaFT",
	filename="generateWorkflowFT.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use KandarpParmar16/GenerateWorkflowGemmaFT with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KandarpParmar16/GenerateWorkflowGemmaFT
# Run inference directly in the terminal:
llama cli -hf KandarpParmar16/GenerateWorkflowGemmaFT

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KandarpParmar16/GenerateWorkflowGemmaFT
# Run inference directly in the terminal:
llama cli -hf KandarpParmar16/GenerateWorkflowGemmaFT

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 KandarpParmar16/GenerateWorkflowGemmaFT
# Run inference directly in the terminal:
./llama-cli -hf KandarpParmar16/GenerateWorkflowGemmaFT

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 KandarpParmar16/GenerateWorkflowGemmaFT
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KandarpParmar16/GenerateWorkflowGemmaFT

Use Docker

docker model run hf.co/KandarpParmar16/GenerateWorkflowGemmaFT

LM Studio
Jan
Ollama
How to use KandarpParmar16/GenerateWorkflowGemmaFT with Ollama:
```
ollama run hf.co/KandarpParmar16/GenerateWorkflowGemmaFT
```

Unsloth Studio

How to use KandarpParmar16/GenerateWorkflowGemmaFT 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 KandarpParmar16/GenerateWorkflowGemmaFT 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 KandarpParmar16/GenerateWorkflowGemmaFT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KandarpParmar16/GenerateWorkflowGemmaFT to start chatting

Atomic Chat new
Docker Model Runner
How to use KandarpParmar16/GenerateWorkflowGemmaFT with Docker Model Runner:
```
docker model run hf.co/KandarpParmar16/GenerateWorkflowGemmaFT
```

Lemonade

How to use KandarpParmar16/GenerateWorkflowGemmaFT with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KandarpParmar16/GenerateWorkflowGemmaFT

Run and chat with the model

lemonade run user.GenerateWorkflowGemmaFT-{{QUANT_TAG}}

List all available models

lemonade list

GenerateWorkflowGemmaFT

File size: 727 Bytes

febe626

{
  "alpha_pattern": {},
  "auto_mapping": null,
  "base_model_name_or_path": "unsloth/gemma-2-9b-bnb-4bit",
  "bias": "none",
  "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": 16,
  "lora_dropout": 0,
  "megatron_config": null,
  "megatron_core": "megatron.core",
  "modules_to_save": null,
  "peft_type": "LORA",
  "r": 16,
  "rank_pattern": {},
  "revision": null,
  "target_modules": [
    "up_proj",
    "k_proj",
    "v_proj",
    "down_proj",
    "gate_proj",
    "o_proj",
    "q_proj"
  ],
  "task_type": "CAUSAL_LM",
  "use_dora": false,
  "use_rslora": false
}