Instructions to use DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF",
	filename="llama3.1_8b_chat_brainstorm-v3.1.Q2_K.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 DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF: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 DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF: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 DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF with Ollama:
```
ollama run hf.co/DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF 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 DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF 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 DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF to start chatting

Docker Model Runner
How to use DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF with Docker Model Runner:
```
docker model run hf.co/DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF:Q4_K_M
```

Lemonade

How to use DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DevQuasar/llama3.1_8b_chat_brainstorm-v3.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.llama3.1_8b_chat_brainstorm-v3.1-GGUF-Q4_K_M

List all available models

lemonade list

llama3.1_8b_chat_brainstorm-v3.1-GGUF / brainstorm.preset.json

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	{
	"name": "brainstorm",
	"load_params": {
	"n_ctx": 1500,
	"n_batch": 512,
	"rope_freq_base": 0,
	"rope_freq_scale": 0,
	"n_gpu_layers": 1,
	"use_mlock": true,
	"main_gpu": 0,
	"tensor_split": [
	0
	],
	"seed": -1,
	"f16_kv": true,
	"use_mmap": true
	},
	"inference_params": {
	"n_threads": 4,
	"n_predict": -1,
	"top_k": 40,
	"top_p": 0.95,
	"temp": 0.1,
	"repeat_penalty": 1.1,
	"input_prefix": "### Instruction:\\n",
	"input_suffix": "\\n### Response:\\n",
	"antiprompt": [
	"### Instruction:"
	],
	"pre_prompt": "",
	"pre_prompt_suffix": "\\n",
	"pre_prompt_prefix": "",
	"seed": -1,
	"tfs_z": 1,
	"typical_p": 1,
	"repeat_last_n": 64,
	"frequency_penalty": 0,
	"presence_penalty": 0,
	"n_keep": 0,
	"logit_bias": {},
	"mirostat": 0,
	"mirostat_tau": 5,
	"mirostat_eta": 0.1,
	"memory_f16": true,
	"multiline_input": false,
	"penalize_nl": true,
	"min_p": 0.05
	}
	}