Instructions to use as-cle-bert/tiny-fungal-llama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use as-cle-bert/tiny-fungal-llama with Transformers:

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

pipe = pipeline("text-generation", model="as-cle-bert/tiny-fungal-llama")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("as-cle-bert/tiny-fungal-llama")
model = AutoModelForCausalLM.from_pretrained("as-cle-bert/tiny-fungal-llama")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use as-cle-bert/tiny-fungal-llama with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "as-cle-bert/tiny-fungal-llama"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "as-cle-bert/tiny-fungal-llama",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/as-cle-bert/tiny-fungal-llama

SGLang

How to use as-cle-bert/tiny-fungal-llama 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 "as-cle-bert/tiny-fungal-llama" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "as-cle-bert/tiny-fungal-llama",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "as-cle-bert/tiny-fungal-llama" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "as-cle-bert/tiny-fungal-llama",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use as-cle-bert/tiny-fungal-llama with Docker Model Runner:
```
docker model run hf.co/as-cle-bert/tiny-fungal-llama
```

tiny-fungal-llama

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.0136

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: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3.0

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	27	1.7950
No log	2.0	54	1.8646
No log	3.0	81	2.0136

Framework versions

Transformers 4.38.1
Pytorch 2.1.2+cpu
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 5

Safetensors

Model size

1B params

Tensor type

F32

Model tree for as-cle-bert/tiny-fungal-llama

Base model

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Finetuned

(554)

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tiny-fungal-llama

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for as-cle-bert/tiny-fungal-llama

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