Instructions to use touqir/Cyrax-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use touqir/Cyrax-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="touqir/Cyrax-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("touqir/Cyrax-7B") model = AutoModelForCausalLM.from_pretrained("touqir/Cyrax-7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use touqir/Cyrax-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "touqir/Cyrax-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "touqir/Cyrax-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/touqir/Cyrax-7B
- SGLang
How to use touqir/Cyrax-7B 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 "touqir/Cyrax-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "touqir/Cyrax-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "touqir/Cyrax-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "touqir/Cyrax-7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use touqir/Cyrax-7B with Docker Model Runner:
docker model run hf.co/touqir/Cyrax-7B
Cyrax-7B
π Evaluation
Open LLM Leaderboard
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|---|---|---|---|---|---|---|---|
| Cyrax-7B | 75.98 | 72.95 | 88.19 | 64.6 | 77.01 | 83.9 | 69.22 |
| Qwen-72B | 73.6 | 65.19 | 85.94 | 77.37 | 60.19 | 82.48 | 70.43 |
| Mixtral-8x7B-Instruct-v0.1-DPO | 73.44 | 69.8 | 87.83 | 71.05 | 69.18 | 81.37 | 61.41 |
| Mixtral-8x7B-Instruct-v0.1 | 72.7 | 70.14 | 87.55 | 71.4 | 64.98 | 81.06 | 61.11 |
| llama2_70b_mmlu | 68.24 | 65.61 | 87.37 | 71.89 | 49.15 | 82.4 | 52.99 |
| falcon-180B | 67.85 | 69.45 | 88.86 | 70.5 | 45.47 | 86.9 | 45.94 |
π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "touqir/Cyrax-7B"
messages = [{"role": "user", "content": "What is Huggingface?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
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