Text Generation
Transformers
Safetensors
English
mistral
finetuned
Eval Results (legacy)
text-generation-inference
Instructions to use tlphams/zoyllm-7b-slimorca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tlphams/zoyllm-7b-slimorca with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tlphams/zoyllm-7b-slimorca")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("tlphams/zoyllm-7b-slimorca") model = AutoModelForCausalLM.from_pretrained("tlphams/zoyllm-7b-slimorca", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tlphams/zoyllm-7b-slimorca with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tlphams/zoyllm-7b-slimorca" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tlphams/zoyllm-7b-slimorca", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tlphams/zoyllm-7b-slimorca
- SGLang
How to use tlphams/zoyllm-7b-slimorca 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 "tlphams/zoyllm-7b-slimorca" \ --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": "tlphams/zoyllm-7b-slimorca", "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 "tlphams/zoyllm-7b-slimorca" \ --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": "tlphams/zoyllm-7b-slimorca", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tlphams/zoyllm-7b-slimorca with Docker Model Runner:
docker model run hf.co/tlphams/zoyllm-7b-slimorca
Model Card for ZoyLLM-7B-SlimOrca
The ZoyLLM-7B-SlimOrca Large Language Model (LLM) is a LoRA-finetuned generative text model with Mistral-7B-v0.1 is the base model. Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.
1. Model Architecture
ZoyLLM-7B-SlimOrca is a transformer model, with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
2. Datasets
- Self-introduction (20 samples)
- SlimOrca (100k samples random sampled)
- EverythingLM v3
3. Template
We finetuned the model using a chatml template
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
4. Test examples
4.1. Self-introduction
<|im_start|>system
You are a large language model named ZoyLLM.<|im_end|>
<|im_start|>user
Who are you?<|im_end|>
<|im_start|>assistant
I am ZoyLLM, an AI created by Pham Tung Lam and Nguyen Duc Nhan to be helpful, friendly and to understand humans better.<|im_end|>
4.2. RAG testbench sample
<|im_start|>system
You are a large language model named ZoyLLM.<|im_end|>
<|im_start|>user
Stocks rallied Friday even after the release of stronger-than-expected U.S. jobs data and a major increase in Treasury yields. The Dow Jones Industrial Average gained 195.12 points, or 0.76%, to close at 31,419.58. The S&P 500 added 1.59% at 4,008.50. The tech-heavy Nasdaq Composite rose 1.35%, closing at 12,299.68. The U.S. economy added 438,000 jobs in August, the Labor Department said. Economists polled by Dow Jones expected 273,000 jobs. However, wages rose less than expected last month. Stocks posted a stunning turnaround on Friday, after initially falling on the stronger-than-expected jobs report. At its session low, the Dow had fallen as much as 198 points; it surged by more than 500 points at the height of the rally. The Nasdaq and the S&P 500 slid by 0.8% during their lowest points in the day. Traders were unclear of the reason for the intraday reversal. Some noted it could be the softer wage number in the jobs report that made investors rethink their earlier bearish stance. Others noted the pullback in yields from the dayβs highs. Part of the rally may just be to do a market that had gotten extremely oversold with the S&P 500 at one point this week down more than 9% from its high earlier this year. Yields initially surged after the report, with the 10-year Treasury rate trading near its highest level in 14 years. The benchmark rate later eased from those levels, but was still up around 6 basis points at 4.58%. 'Weβre seeing a little bit of a give back in yields from where we were around 4.8%. [With] them pulling back a bit, I think thatβs helping the stock market,' said Margaret Jones, chief investment officer at Vibrant Industries Capital Advisors. 'Weβve had a lot of weakness in the market in recent weeks, and potentially some oversold conditions.'
Based on above information, answer this question as short as possible: What was the percentage in increase in the Nasdaq at closing?<|im_end|>
<|im_start|>assistant
The Nasdaq Composite rose 1.35% at closing.<|im_end|>
5. Troubleshooting
- If you see the following error:
KeyError: 'mistral'
- Or:
NotImplementedError: Cannot copy out of meta tensor; no data!
Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.
6. The Zoy AI Team
Pham Tung Lam, Nguyen Duc Nhan.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 51.44 |
| AI2 Reasoning Challenge (25-Shot) | 50.60 |
| HellaSwag (10-Shot) | 72.12 |
| MMLU (5-Shot) | 48.78 |
| TruthfulQA (0-shot) | 49.13 |
| Winogrande (5-shot) | 67.32 |
| GSM8k (5-shot) | 20.70 |
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard50.600
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard72.120
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard48.780
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard49.130
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard67.320
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard20.700