Instructions to use aloobun/Reyna-Mini-1.8B-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aloobun/Reyna-Mini-1.8B-v0.2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aloobun/Reyna-Mini-1.8B-v0.2", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("aloobun/Reyna-Mini-1.8B-v0.2", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("aloobun/Reyna-Mini-1.8B-v0.2", trust_remote_code=True) 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use aloobun/Reyna-Mini-1.8B-v0.2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aloobun/Reyna-Mini-1.8B-v0.2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aloobun/Reyna-Mini-1.8B-v0.2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/aloobun/Reyna-Mini-1.8B-v0.2
- SGLang
How to use aloobun/Reyna-Mini-1.8B-v0.2 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 "aloobun/Reyna-Mini-1.8B-v0.2" \ --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": "aloobun/Reyna-Mini-1.8B-v0.2", "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 "aloobun/Reyna-Mini-1.8B-v0.2" \ --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": "aloobun/Reyna-Mini-1.8B-v0.2", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use aloobun/Reyna-Mini-1.8B-v0.2 with Docker Model Runner:
docker model run hf.co/aloobun/Reyna-Mini-1.8B-v0.2
- Finetuned Qwen/Qwen1.5-1.8B-Chat, with SFT on Hercules v3 dataset.
- This marks the third model in this series.
- Format: ChatML -
<|im_start|>system {system}<|im_end|> <|im_start|>user {prompt}<|im_end|> <|im_start|>assistant - Next step would be to do a DPO train on top.
Benchamrks:
| Avg. | Arc | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|---|---|---|---|---|---|---|
| 45.94 | 36.6 | 60.19 | 44.75 | 41.24 | 61.56 | 31.31 |
Example:
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, StoppingCriteria
import torch
class MyStoppingCriteria(StoppingCriteria):
def __init__(self, target_sequence, prompt):
self.target_sequence = target_sequence
self.prompt=prompt
def __call__(self, input_ids, scores, **kwargs):
generated_text = tokenizer.decode(input_ids[0])
generated_text = generated_text.replace(self.prompt,'')
if self.target_sequence in generated_text:
return True
return False
def __len__(self):
return 1
def __iter__(self):
yield self
modelpath="aloobun/Reyna-Mini-1.8B-v0.2"
model = AutoModelForCausalLM.from_pretrained(
modelpath,
torch_dtype=torch.bfloat16,
device_map="cuda",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
modelpath,
trust_remote_code=True,
use_fast=False,
)
prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nIs there inherent order in nature or is it all chaos and chance?<|im_end|>\n<|im_start|>assistant\n"
encoded_input = tokenizer(prompt, return_tensors='pt')
input_ids=encoded_input['input_ids'].cuda()
streamer = TextStreamer(tokenizer=tokenizer, skip_prompt=True)
op = model.generate(
input_ids,
streamer=streamer,
pad_token_id=tokenizer.eos_token_id,
do_sample=True,
temperature=0.6,
top_p=0.8,
max_new_tokens=512,
stopping_criteria=MyStoppingCriteria("<|im_end|>", prompt)
)
Output:
Nature appears to be inherently organized, with patterns and structures that can be observed across different levels of organization. However, the exact mechanisms by which these patterns emerge and evolve remain largely unknown. The universe seems to be governed by a series of laws and principles known as "laws of physics," such as Newton's laws of motion, electromagnetism, and thermodynamics. These laws govern how matter and energy interact with each other and how they behave over time. Despite our understanding of these laws, we still struggle to comprehend the underlying mechanisms that allow for the emergence of complex patterns and structures. This is because the universe operates on a scale that is too small for us to observe directly, and therefore we cannot fully understand its internal workings. In summary, while there may be some level of order and structure within the universe, the precise mechanisms governing this order remain largely unknown.<|im_end|>
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 45.94 |
| AI2 Reasoning Challenge (25-Shot) | 36.60 |
| HellaSwag (10-Shot) | 60.19 |
| MMLU (5-Shot) | 44.75 |
| TruthfulQA (0-shot) | 41.24 |
| Winogrande (5-shot) | 61.56 |
| GSM8k (5-shot) | 31.31 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard36.600
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard60.190
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard44.750
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard41.240
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard61.560
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard31.310
