Instructions to use kashif/stack-llama-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kashif/stack-llama-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kashif/stack-llama-2")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kashif/stack-llama-2") model = AutoModelForCausalLM.from_pretrained("kashif/stack-llama-2") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use kashif/stack-llama-2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kashif/stack-llama-2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kashif/stack-llama-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/kashif/stack-llama-2
- SGLang
How to use kashif/stack-llama-2 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 "kashif/stack-llama-2" \ --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": "kashif/stack-llama-2", "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 "kashif/stack-llama-2" \ --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": "kashif/stack-llama-2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use kashif/stack-llama-2 with Docker Model Runner:
docker model run hf.co/kashif/stack-llama-2
Update README.md
Browse files
README.md
CHANGED
|
@@ -13,3 +13,15 @@ tags:
|
|
| 13 |
# Stack-Llama-2
|
| 14 |
|
| 15 |
DPO fine-tuned [Llama-2 7B model](https://huggingface.co/meta-llama/Llama-2-7b). The model is designed to generate human-like responses to questions in Stack Exchange domains of programming, mathematics, physics, and more. For more info check out the [blog post](https://huggingface.co/blog/dpo-trl) and github [example](https://github.com/lvwerra/trl/tree/main/examples/research_projects/stack_llama_2/scripts).
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 13 |
# Stack-Llama-2
|
| 14 |
|
| 15 |
DPO fine-tuned [Llama-2 7B model](https://huggingface.co/meta-llama/Llama-2-7b). The model is designed to generate human-like responses to questions in Stack Exchange domains of programming, mathematics, physics, and more. For more info check out the [blog post](https://huggingface.co/blog/dpo-trl) and github [example](https://github.com/lvwerra/trl/tree/main/examples/research_projects/stack_llama_2/scripts).
|
| 16 |
+
|
| 17 |
+
|
| 18 |
+
## Trianing Details
|
| 19 |
+
|
| 20 |
+
### Training Data
|
| 21 |
+
|
| 22 |
+
Original datasets are described in [the LLaMA Model Card](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md#training-dataset).
|
| 23 |
+
Fine-tuning datasets for this model are based on [Stack Exchange Paired](https://huggingface.co/datasets/lvwerra/stack-exchange-paired), which consists of questions and answers from various domains in Stack Exchange, such as programming, mathematics, physics, and more. Specifically:
|
| 24 |
+
|
| 25 |
+
**Traditional Fine-tuning:** [https://huggingface.co/datasets/lvwerra/stack-exchange-paired/tree/main/data/finetune](https://huggingface.co/datasets/lvwerra/stack-exchange-paired/tree/main/data/finetune)
|
| 26 |
+
|
| 27 |
+
**DPO Training:** [https://huggingface.co/datasets/lvwerra/stack-exchange-paired/tree/main/data/rl](https://huggingface.co/datasets/lvwerra/stack-exchange-paired/tree/main/data/rl)
|