Text Generation
Transformers
Safetensors
mixtral
llama-factory
lora
Generated from Trainer
text-generation-inference
Instructions to use fblgit/UNAversal-2x7B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fblgit/UNAversal-2x7B-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fblgit/UNAversal-2x7B-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("fblgit/UNAversal-2x7B-v1") model = AutoModelForCausalLM.from_pretrained("fblgit/UNAversal-2x7B-v1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use fblgit/UNAversal-2x7B-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fblgit/UNAversal-2x7B-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fblgit/UNAversal-2x7B-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fblgit/UNAversal-2x7B-v1
- SGLang
How to use fblgit/UNAversal-2x7B-v1 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 "fblgit/UNAversal-2x7B-v1" \ --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": "fblgit/UNAversal-2x7B-v1", "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 "fblgit/UNAversal-2x7B-v1" \ --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": "fblgit/UNAversal-2x7B-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fblgit/UNAversal-2x7B-v1 with Docker Model Runner:
docker model run hf.co/fblgit/UNAversal-2x7B-v1
UNAversal-2x7B-v1
Merely Phase 1 UNA, only MLP's and its kinda of a beta. The goal was to produce a small but powerful MoE.
This is a 2 MoE model, of 7B each expert. Based on intel-neural series v3.
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| arc_challenge | Yaml | none | 25 | acc | 0.7133 | ± | 0.0132 |
| none | 25 | acc_norm | 0.7235 | ± | 0.0131 | ||
| arc_easy | Yaml | none | 0 | acc | 0.8674 | ± | 0.0070 |
| none | 0 | acc_norm | 0.8291 | ± | 0.0077 | ||
| boolq | Yaml | none | 0 | acc | 0.8768 | ± | 0.0057 |
| lambada_openai | Yaml | none | 0 | perplexity | 3.6656 | ± | 0.0841 |
| none | 0 | acc | 0.7017 | ± | 0.0064 | ||
| mathqa | Yaml | none | 0 | acc | 0.3474 | ± | 0.0087 |
| none | 0 | acc_norm | 0.3585 | ± | 0.0088 | ||
| piqa | Yaml | none | 0 | acc | 0.8411 | ± | 0.0085 |
| none | 0 | acc_norm | 0.8526 | ± | 0.0083 | ||
| sciq | Yaml | none | 0 | acc | 0.9600 | ± | 0.0062 |
| none | 0 | acc_norm | 0.9370 | ± | 0.0077 |
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