Instructions to use NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned with Transformers:

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

pipe = pipeline("question-answering", model="NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned")
model = AutoModelForCausalLM.from_pretrained("NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned 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 NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned: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 NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned: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 NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned:Q4_K_M

Use Docker

docker model run hf.co/NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned:Q4_K_M

LM Studio
Jan
Ollama
How to use NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned with Ollama:
```
ollama run hf.co/NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned:Q4_K_M
```

Unsloth Studio

How to use NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned 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 NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned 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 NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned to start chatting

Docker Model Runner
How to use NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned with Docker Model Runner:
```
docker model run hf.co/NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned:Q4_K_M
```

Lemonade

How to use NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned:Q4_K_M

Run and chat with the model

lemonade run user.llama31-8bn_Reinforcement-Fine-Tuned-Q4_K_M

List all available models

lemonade list

Atomic Chat

llama31-8bn_Reinforcement-Fine-Tuned : GGUF

This model was finetuned and converted to GGUF format using Unsloth.

Example usage:

For text only LLMs: llama-cli -hf NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned --jinja
For multimodal models: llama-mtmd-cli -hf NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned --jinja

Available Model files:

meta-llama-3.1-8b.Q5_K_M.gguf
meta-llama-3.1-8b.Q8_0.gguf
meta-llama-3.1-8b.Q4_K_M.gguf This was trained 2x faster with Unsloth

Downloads last month: 29

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned

Base model

meta-llama/Llama-3.1-8B

Quantized

(326)

this model

NamrataThakur
/

llama31-8bn_Reinforcement-Fine-Tuned

llama31-8bn_Reinforcement-Fine-Tuned : GGUF

Available Model files:

Model tree for NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned

Dataset used to train NamrataThakur/llama31-8bn_Reinforcement-Fine-Tuned