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

Libraries

How to use LucileFavero/AMmodel with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("LucileFavero/AMmodel", dtype="auto")

llama-cpp-python

How to use LucileFavero/AMmodel with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="LucileFavero/AMmodel",
	filename="unsloth.Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use LucileFavero/AMmodel with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LucileFavero/AMmodel:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LucileFavero/AMmodel:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LucileFavero/AMmodel:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LucileFavero/AMmodel:Q4_K_M

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 LucileFavero/AMmodel:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LucileFavero/AMmodel:Q4_K_M

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 LucileFavero/AMmodel:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LucileFavero/AMmodel:Q4_K_M

Use Docker

docker model run hf.co/LucileFavero/AMmodel:Q4_K_M

LM Studio
Jan
Ollama
How to use LucileFavero/AMmodel with Ollama:
```
ollama run hf.co/LucileFavero/AMmodel:Q4_K_M
```

Unsloth Studio new

How to use LucileFavero/AMmodel 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 LucileFavero/AMmodel 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 LucileFavero/AMmodel to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use LucileFavero/AMmodel with Docker Model Runner:
```
docker model run hf.co/LucileFavero/AMmodel:Q4_K_M
```

Lemonade

How to use LucileFavero/AMmodel with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull LucileFavero/AMmodel:Q4_K_M

Run and chat with the model

lemonade run user.AMmodel-Q4_K_M

List all available models

lemonade list

LucileFavero commited on Nov 1, 2024

Commit

4b9f12c

verified ·

1 Parent(s): 946e561

Upload model trained with Unsloth

Browse files

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Files changed (2) hide show

adapter_config.json +34 -0
adapter_model.safetensors +3 -0

adapter_config.json ADDED Viewed

	@@ -0,0 +1,34 @@

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+  "base_model_name_or_path": "unsloth/meta-llama-3.1-8b-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",
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+  "peft_type": "LORA",
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+  "rank_pattern": {},
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+    "up_proj",
+    "k_proj",
+    "down_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

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