Instructions to use SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora with Transformers:

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

pipe = pipeline("text-generation", model="SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora")
model = AutoModelForCausalLM.from_pretrained("SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora", device_map="auto")
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 SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora

SGLang

How to use SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora 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 "SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora" \
    --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": "SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora",
		"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 "SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora" \
        --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": "SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora with Docker Model Runner:
```
docker model run hf.co/SebastianSchramm/tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora
```

tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora / README.md

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	---
	tags:
	- generated_from_trainer
	model-index:
	- name: tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora
	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. -->

	# tinyllama-1.1B-intermediate-step-715k-1.5T-dpo-lora

	This model was trained from scratch on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.6877
	- Rewards/chosen: 0.0254
	- Rewards/rejected: 0.0135
	- Rewards/accuracies: 0.5645
	- Rewards/margins: 0.0119
	- Logps/rejected: -315.6106
	- Logps/chosen: -402.4017
	- Logits/rejected: -4.0818
	- Logits/chosen: -4.1916

	## 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: 5e-07
	- train_batch_size: 2
	- eval_batch_size: 4
	- seed: 42
	- distributed_type: multi-GPU
	- gradient_accumulation_steps: 32
	- total_train_batch_size: 64
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 3

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Rewards/chosen \| Rewards/rejected \| Rewards/accuracies \| Rewards/margins \| Logps/rejected \| Logps/chosen \| Logits/rejected \| Logits/chosen \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------:\|:----------------:\|:------------------:\|:---------------:\|:--------------:\|:------------:\|:---------------:\|:-------------:\|
	\| 0.6916 \| 1.0 \| 968 \| 0.6921 \| 0.0039 \| 0.0011 \| 0.5070 \| 0.0028 \| -315.7343 \| -402.6164 \| -4.0813 \| -4.1913 \|
	\| 0.6904 \| 2.0 \| 1936 \| 0.6884 \| 0.0191 \| 0.0086 \| 0.5570 \| 0.0105 \| -315.6588 \| -402.4643 \| -4.0824 \| -4.1920 \|
	\| 0.6876 \| 3.0 \| 2904 \| 0.6877 \| 0.0254 \| 0.0135 \| 0.5645 \| 0.0119 \| -315.6106 \| -402.4017 \| -4.0818 \| -4.1916 \|


	### Framework versions

	- Transformers 4.35.0
	- Pytorch 2.1.0+cu121
	- Datasets 2.14.6
	- Tokenizers 0.14.1