Instructions to use pansophic/rocket-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use pansophic/rocket-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="pansophic/rocket-3B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("pansophic/rocket-3B") model = AutoModelForCausalLM.from_pretrained("pansophic/rocket-3B") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use pansophic/rocket-3B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "pansophic/rocket-3B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "pansophic/rocket-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/pansophic/rocket-3B
- SGLang
How to use pansophic/rocket-3B 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 "pansophic/rocket-3B" \ --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": "pansophic/rocket-3B", "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 "pansophic/rocket-3B" \ --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": "pansophic/rocket-3B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use pansophic/rocket-3B with Docker Model Runner:
docker model run hf.co/pansophic/rocket-3B
Adding Evaluation Results
#10
by leaderboard-pr-bot - opened
README.md
CHANGED
|
@@ -1,11 +1,11 @@
|
|
| 1 |
---
|
| 2 |
-
model-index:
|
| 3 |
-
- name: rocket-3b
|
| 4 |
-
results: []
|
| 5 |
-
license: cc-by-sa-4.0
|
| 6 |
language:
|
| 7 |
- en
|
|
|
|
| 8 |
base_model: stabilityai/stablelm-3b-4e1t
|
|
|
|
|
|
|
|
|
|
| 9 |
---
|
| 10 |
|
| 11 |
<img src="https://cdn-uploads.huggingface.co/production/uploads/6501bfe0493fd9c8c2e32402/BmbkjOkcTm-YMa-unolmJ.png" alt="Rocket Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
|
|
@@ -128,4 +128,17 @@ The pretraining dataset is comprised of a filtered mixture of open-source large-
|
|
| 128 |
|
| 129 |
**The model name is inspired by the small but formidable character from 'Guardians of the Galaxy'. Similar to its namesake, this model, with its 3 billion parameters, showcases remarkable efficiency and effectiveness, challenging larger models despite its smaller size."*
|
| 130 |
|
| 131 |
-
*Model card adapted from [Zephyr Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta/blob/main/README.md) and [Tulu-2-7B](https://huggingface.co/allenai/tulu-2-7b/blob/main/README.md)*
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
| 2 |
language:
|
| 3 |
- en
|
| 4 |
+
license: cc-by-sa-4.0
|
| 5 |
base_model: stabilityai/stablelm-3b-4e1t
|
| 6 |
+
model-index:
|
| 7 |
+
- name: rocket-3b
|
| 8 |
+
results: []
|
| 9 |
---
|
| 10 |
|
| 11 |
<img src="https://cdn-uploads.huggingface.co/production/uploads/6501bfe0493fd9c8c2e32402/BmbkjOkcTm-YMa-unolmJ.png" alt="Rocket Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
|
|
|
|
| 128 |
|
| 129 |
**The model name is inspired by the small but formidable character from 'Guardians of the Galaxy'. Similar to its namesake, this model, with its 3 billion parameters, showcases remarkable efficiency and effectiveness, challenging larger models despite its smaller size."*
|
| 130 |
|
| 131 |
+
*Model card adapted from [Zephyr Beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta/blob/main/README.md) and [Tulu-2-7B](https://huggingface.co/allenai/tulu-2-7b/blob/main/README.md)*
|
| 132 |
+
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
|
| 133 |
+
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_pansophic__rocket-3B)
|
| 134 |
+
|
| 135 |
+
| Metric |Value|
|
| 136 |
+
|---------------------------------|----:|
|
| 137 |
+
|Avg. |55.77|
|
| 138 |
+
|AI2 Reasoning Challenge (25-Shot)|50.60|
|
| 139 |
+
|HellaSwag (10-Shot) |76.69|
|
| 140 |
+
|MMLU (5-Shot) |47.10|
|
| 141 |
+
|TruthfulQA (0-shot) |55.82|
|
| 142 |
+
|Winogrande (5-shot) |67.96|
|
| 143 |
+
|GSM8k (5-shot) |36.47|
|
| 144 |
+
|