Text Generation
Transformers
PyTorch
Safetensors
mistral
Generated from Trainer
text-generation-inference
Instructions to use NovoCode/Novocode7b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NovoCode/Novocode7b-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NovoCode/Novocode7b-v2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NovoCode/Novocode7b-v2") model = AutoModelForCausalLM.from_pretrained("NovoCode/Novocode7b-v2") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use NovoCode/Novocode7b-v2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NovoCode/Novocode7b-v2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NovoCode/Novocode7b-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/NovoCode/Novocode7b-v2
- SGLang
How to use NovoCode/Novocode7b-v2 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 "NovoCode/Novocode7b-v2" \ --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": "NovoCode/Novocode7b-v2", "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 "NovoCode/Novocode7b-v2" \ --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": "NovoCode/Novocode7b-v2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use NovoCode/Novocode7b-v2 with Docker Model Runner:
docker model run hf.co/NovoCode/Novocode7b-v2
Adding Evaluation Results (#2)
Browse files- Adding Evaluation Results (ae40649e867ee658ac2cb63a9d4a593a4acb4afe)
Co-authored-by: Open LLM Leaderboard PR Bot <leaderboard-pr-bot@users.noreply.huggingface.co>
README.md
CHANGED
|
@@ -1,10 +1,10 @@
|
|
| 1 |
---
|
|
|
|
| 2 |
tags:
|
| 3 |
- generated_from_trainer
|
| 4 |
model-index:
|
| 5 |
- name: out
|
| 6 |
results: []
|
| 7 |
-
license: apache-2.0
|
| 8 |
---
|
| 9 |
|
| 10 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
@@ -132,3 +132,17 @@ The following hyperparameters were used during training:
|
|
| 132 |
- Pytorch 2.0.1+cu117
|
| 133 |
- Datasets 2.16.1
|
| 134 |
- Tokenizers 0.15.0
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
tags:
|
| 4 |
- generated_from_trainer
|
| 5 |
model-index:
|
| 6 |
- name: out
|
| 7 |
results: []
|
|
|
|
| 8 |
---
|
| 9 |
|
| 10 |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
|
|
|
| 132 |
- Pytorch 2.0.1+cu117
|
| 133 |
- Datasets 2.16.1
|
| 134 |
- Tokenizers 0.15.0
|
| 135 |
+
|
| 136 |
+
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
|
| 137 |
+
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_NovoCode__Novocode7b-v2)
|
| 138 |
+
|
| 139 |
+
| Metric |Value|
|
| 140 |
+
|---------------------------------|----:|
|
| 141 |
+
|Avg. |56.57|
|
| 142 |
+
|AI2 Reasoning Challenge (25-Shot)|61.01|
|
| 143 |
+
|HellaSwag (10-Shot) |84.12|
|
| 144 |
+
|MMLU (5-Shot) |64.05|
|
| 145 |
+
|TruthfulQA (0-shot) |42.21|
|
| 146 |
+
|Winogrande (5-shot) |79.87|
|
| 147 |
+
|GSM8k (5-shot) | 8.19|
|
| 148 |
+
|