Instructions to use ayushrupapara/llama3_8b_flanv2_cot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ayushrupapara/llama3_8b_flanv2_cot with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("question-answering", model="ayushrupapara/llama3_8b_flanv2_cot")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ayushrupapara/llama3_8b_flanv2_cot", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ayushrupapara/llama3_8b_flanv2_cot 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 ayushrupapara/llama3_8b_flanv2_cot:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ayushrupapara/llama3_8b_flanv2_cot:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ayushrupapara/llama3_8b_flanv2_cot:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ayushrupapara/llama3_8b_flanv2_cot: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 ayushrupapara/llama3_8b_flanv2_cot:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ayushrupapara/llama3_8b_flanv2_cot: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 ayushrupapara/llama3_8b_flanv2_cot:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ayushrupapara/llama3_8b_flanv2_cot:Q4_K_M

Use Docker

docker model run hf.co/ayushrupapara/llama3_8b_flanv2_cot:Q4_K_M

LM Studio
Jan
Ollama
How to use ayushrupapara/llama3_8b_flanv2_cot with Ollama:
```
ollama run hf.co/ayushrupapara/llama3_8b_flanv2_cot:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use ayushrupapara/llama3_8b_flanv2_cot with Docker Model Runner:
```
docker model run hf.co/ayushrupapara/llama3_8b_flanv2_cot:Q4_K_M
```

Lemonade

How to use ayushrupapara/llama3_8b_flanv2_cot with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ayushrupapara/llama3_8b_flanv2_cot:Q4_K_M

Run and chat with the model

lemonade run user.llama3_8b_flanv2_cot-Q4_K_M

List all available models

lemonade list

Atomic Chat

Code

https://github.com/AJlearner46/Deduplicate-flanv2-finetune-LLaMa3-

Uploaded model

Developed by: ayushrupapara
License: apache-2.0
Finetuned from model : unsloth/llama-3-8b-bnb-4bit

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 32

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for ayushrupapara/llama3_8b_flanv2_cot

Base model

meta-llama/Meta-Llama-3-8B

Quantized

unsloth/llama-3-8b-bnb-4bit

Quantized

(872)

this model

ayushrupapara
/

llama3_8b_flanv2_cot

Code

Uploaded model

Model tree for ayushrupapara/llama3_8b_flanv2_cot

Dataset used to train ayushrupapara/llama3_8b_flanv2_cot