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

llama3_8b_flanv2_cot

File size: 739 Bytes

---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
datasets:
- ayushrupapara/flanv2_cot_dedepulicated
pipeline_tag: question-answering
---
# 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](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)