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
# Warning: Pipeline type "question-answering" is no longer supported in transformers v5.
# You must load the model directly (see below) or downgrade to v4.x with:
# pip install "transformers<5.0.0"
from transformers import pipeline

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

# pip install -U transformers accelerate
# 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 / llama3_8b_flanv2_cot-unsloth.Q4_K_M.gguf

ayushrupapara

(Trained with Unsloth)

9202958 verified over 2 years ago

History Blame

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