Text Classification
Transformers
Safetensors
GGUF
English
t5
text2text-generation
cefr
language-learning
education
Instructions to use balastml/COPAL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use balastml/COPAL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="balastml/COPAL")# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("balastml/COPAL") model = AutoModelForSeq2SeqLM.from_pretrained("balastml/COPAL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use balastml/COPAL 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 balastml/COPAL:F16 # Run inference directly in the terminal: llama cli -hf balastml/COPAL:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf balastml/COPAL:F16 # Run inference directly in the terminal: llama cli -hf balastml/COPAL:F16
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 balastml/COPAL:F16 # Run inference directly in the terminal: ./llama-cli -hf balastml/COPAL:F16
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 balastml/COPAL:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf balastml/COPAL:F16
Use Docker
docker model run hf.co/balastml/COPAL:F16
- LM Studio
- Jan
- Ollama
How to use balastml/COPAL with Ollama:
ollama run hf.co/balastml/COPAL:F16
- Unsloth Studio
How to use balastml/COPAL 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 balastml/COPAL 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 balastml/COPAL to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for balastml/COPAL to start chatting
- Atomic Chat new
- Docker Model Runner
How to use balastml/COPAL with Docker Model Runner:
docker model run hf.co/balastml/COPAL:F16
- Lemonade
How to use balastml/COPAL with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull balastml/COPAL:F16
Run and chat with the model
lemonade run user.COPAL-F16
List all available models
lemonade list
File size: 846 Bytes
5ff308a | 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 | {
"architectures": [
"T5ForConditionalGeneration"
],
"classifier_dropout": 0.0,
"d_ff": 3072,
"d_kv": 64,
"d_model": 768,
"decoder_start_token_id": 0,
"dense_act_fn": "relu",
"dropout_rate": 0.1,
"dtype": "float32",
"eos_token_id": 1,
"feed_forward_proj": "relu",
"initializer_factor": 1.0,
"is_decoder": false,
"is_encoder_decoder": true,
"is_gated_act": false,
"layer_norm_epsilon": 1e-06,
"model_type": "t5",
"n_positions": 512,
"num_decoder_layers": 12,
"num_heads": 12,
"num_layers": 12,
"output_past": true,
"pad_token_id": 0,
"relative_attention_max_distance": 128,
"relative_attention_num_buckets": 32,
"scale_decoder_outputs": true,
"tie_word_embeddings": true,
"transformers_version": "5.12.1",
"use_cache": true,
"vocab_size": 32128
}
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