Instructions to use smangrul/llama-3-8B-instruct-function-calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use smangrul/llama-3-8B-instruct-function-calling with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/llama-3-8b-Instruct-bnb-4bit") model = PeftModel.from_pretrained(base_model, "smangrul/llama-3-8B-instruct-function-calling") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use smangrul/llama-3-8B-instruct-function-calling 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 smangrul/llama-3-8B-instruct-function-calling:Q4_K_M # Run inference directly in the terminal: llama cli -hf smangrul/llama-3-8B-instruct-function-calling:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf smangrul/llama-3-8B-instruct-function-calling:Q4_K_M # Run inference directly in the terminal: llama cli -hf smangrul/llama-3-8B-instruct-function-calling: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 smangrul/llama-3-8B-instruct-function-calling:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf smangrul/llama-3-8B-instruct-function-calling: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 smangrul/llama-3-8B-instruct-function-calling:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf smangrul/llama-3-8B-instruct-function-calling:Q4_K_M
Use Docker
docker model run hf.co/smangrul/llama-3-8B-instruct-function-calling:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use smangrul/llama-3-8B-instruct-function-calling with Ollama:
ollama run hf.co/smangrul/llama-3-8B-instruct-function-calling:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use smangrul/llama-3-8B-instruct-function-calling with Docker Model Runner:
docker model run hf.co/smangrul/llama-3-8B-instruct-function-calling:Q4_K_M
- Lemonade
How to use smangrul/llama-3-8B-instruct-function-calling with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull smangrul/llama-3-8B-instruct-function-calling:Q4_K_M
Run and chat with the model
lemonade run user.llama-3-8B-instruct-function-calling-Q4_K_M
List all available models
lemonade list
- Atomic Chat
File size: 1,554 Bytes
1d67e6d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | ---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- unsloth
- generated_from_trainer
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
datasets:
- generator
model-index:
- name: llama-3-8B-instruct-function-calling
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-3-8B-instruct-function-calling
This model is a fine-tuned version of [unsloth/llama-3-8b-Instruct-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3908
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.386 | 1.0 | 766 | 0.3908 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2 |