Text Generation
Transformers
TensorBoard
Safetensors
GGUF
llama
Generated from Trainer
function_calling
function-calling
GGUF
text2text-generation
text-generation-inference
Instructions to use archit11/small-function-calling with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use archit11/small-function-calling with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="archit11/small-function-calling")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("archit11/small-function-calling") model = AutoModelForCausalLM.from_pretrained("archit11/small-function-calling") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use archit11/small-function-calling with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "archit11/small-function-calling" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "archit11/small-function-calling", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/archit11/small-function-calling
- SGLang
How to use archit11/small-function-calling with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "archit11/small-function-calling" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "archit11/small-function-calling", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "archit11/small-function-calling" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "archit11/small-function-calling", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use archit11/small-function-calling with Docker Model Runner:
docker model run hf.co/archit11/small-function-calling
File size: 1,671 Bytes
05f3381 a454893 05f3381 a454893 05f3381 7a56d13 05f3381 8a5091c a691d64 05f3381 7a56d13 05f3381 7a56d13 05f3381 a454893 | 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 | ---
library_name: transformers
license: mit
base_model: nisten/Biggie-SmoLlm-0.15B-Base
tags:
- generated_from_trainer
- function_calling
- function-calling
- GGUF
model-index:
- name: capybara_finetuned_results
results: []
datasets:
- NousResearch/hermes-function-calling-v1
pipeline_tag: text2text-generation
---
<!-- 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. -->
# capybara_finetuned_results
This model is a fine-tuned version of [nisten/Biggie-SmoLlm-0.15B-Base](https://huggingface.co/nisten/Biggie-SmoLlm-0.15B-Base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0289
## Model description

<video controls autoplay muted src="https://0x0.st/XYF7.mp4"></video>
## 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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 15
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0284 | 8.4507 | 300 | 0.0289 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0
- Datasets 3.0.0
- Tokenizers 0.19.1 |