Instructions to use Trelis/99-v9 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Trelis/99-v9 with Transformers:

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

pipe = pipeline("text-generation", model="Trelis/99-v9")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Trelis/99-v9")
model = AutoModelForCausalLM.from_pretrained("Trelis/99-v9")
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 Settings

vLLM

How to use Trelis/99-v9 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Trelis/99-v9"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Trelis/99-v9",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Trelis/99-v9

SGLang

How to use Trelis/99-v9 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 "Trelis/99-v9" \
    --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": "Trelis/99-v9",
		"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 "Trelis/99-v9" \
        --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": "Trelis/99-v9",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Trelis/99-v9 with Docker Model Runner:
```
docker model run hf.co/Trelis/99-v9
```

99-v9 / README.md

RonanMcGovern

End of training

3d3683e verified over 1 year ago

preview code

raw

history blame contribute delete

2.62 kB

	---
	library_name: transformers
	license: apache-2.0
	base_model: Trelis/SmolLM-135M-Instruct-layer-pruned-90M-raw
	tags:
	- trl
	- sft
	- generated_from_trainer
	model-index:
	- name: 99-v9
	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. -->

	# 99-v9

	This model is a fine-tuned version of [Trelis/SmolLM-135M-Instruct-layer-pruned-90M-raw](https://huggingface.co/Trelis/SmolLM-135M-Instruct-layer-pruned-90M-raw) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.7495

	## 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: 0.002
	- train_batch_size: 8
	- eval_batch_size: 8
	- seed: 42
	- distributed_type: multi-GPU
	- num_devices: 8
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 256
	- total_eval_batch_size: 64
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.005
	- lr_scheduler_warmup_steps: 89
	- training_steps: 17894

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:------:\|:-----:\|:---------------:\|
	\| 0.6331 \| 0.0500 \| 894 \| 0.6004 \|
	\| 0.5667 \| 0.0999 \| 1788 \| 0.5463 \|
	\| 0.5423 \| 0.1499 \| 2682 \| 0.5138 \|
	\| 0.5749 \| 0.1998 \| 3576 \| 0.7377 \|
	\| 0.5378 \| 0.2498 \| 4470 \| 0.7542 \|
	\| 0.506 \| 0.2998 \| 5364 \| 0.7902 \|
	\| 0.5561 \| 0.3497 \| 6258 \| 0.7810 \|
	\| 0.5259 \| 0.3997 \| 7152 \| 0.7914 \|
	\| 0.5516 \| 0.4496 \| 8046 \| 0.7611 \|
	\| 0.5131 \| 0.4996 \| 8940 \| 0.6860 \|
	\| 0.5069 \| 0.5496 \| 9834 \| 0.7247 \|
	\| 0.4977 \| 0.5995 \| 10728 \| 0.7375 \|
	\| 0.4976 \| 0.6495 \| 11622 \| 0.7436 \|
	\| 0.5018 \| 0.6995 \| 12516 \| 0.7520 \|
	\| 0.537 \| 0.7494 \| 13410 \| 0.7613 \|
	\| 0.5018 \| 0.7994 \| 14304 \| 0.6922 \|
	\| 0.4891 \| 0.8493 \| 15198 \| 0.7322 \|
	\| 0.4808 \| 0.8993 \| 16092 \| 0.7430 \|
	\| 0.5231 \| 0.9493 \| 16986 \| 0.7546 \|
	\| 0.5103 \| 0.9992 \| 17880 \| 0.7495 \|


	### Framework versions

	- Transformers 4.44.2
	- Pytorch 2.1.1+cu121
	- Datasets 3.0.0
	- Tokenizers 0.19.1