Text Generation
Transformers
TensorBoard
Safetensors
llama
Generated from Trainer
custom_code
text-generation-inference
Instructions to use flytech/togetherchat-dev-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use flytech/togetherchat-dev-7b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="flytech/togetherchat-dev-7b", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("flytech/togetherchat-dev-7b", trust_remote_code=True) model = AutoModelForMultimodalLM.from_pretrained("flytech/togetherchat-dev-7b", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use flytech/togetherchat-dev-7b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "flytech/togetherchat-dev-7b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "flytech/togetherchat-dev-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/flytech/togetherchat-dev-7b
- SGLang
How to use flytech/togetherchat-dev-7b 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 "flytech/togetherchat-dev-7b" \ --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": "flytech/togetherchat-dev-7b", "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 "flytech/togetherchat-dev-7b" \ --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": "flytech/togetherchat-dev-7b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use flytech/togetherchat-dev-7b with Docker Model Runner:
docker model run hf.co/flytech/togetherchat-dev-7b
| license: llama2 | |
| base_model: togethercomputer/LLaMA-2-7B-32K | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: togetherchat-dev-7b | |
| results: [] | |
| # togetherchat-dev-7b | |
| This model is a fine-tuned version of [togethercomputer/LLaMA-2-7B-32K](https://huggingface.co/togethercomputer/LLaMA-2-7B-32K) using 5000 examples and 3 datasets: | |
| platypus_dataset = load_dataset("garage-bAInd/Open-Platypus") | |
| codealpaca_dataset = load_dataset("sahil2801/CodeAlpaca-20k") | |
| evol_codealpaca_dataset = load_dataset("theblackcat102/evol-codealpaca-v1") | |
| ## Model description | |
| Step Training Loss | |
| --------------------- | |
| 60 1.293000 | |
| 120 0.673600 | |
| 180 0.633200 | |
| 240 0.611600 | |
| 300 0.633000 | |
| 360 0.589500 | |
| 480 0.587600 | |
| 540 0.569000 | |
| 600 0.548700 | |
| 660 0.553100 | |
| 720 0.531500 | |
| 780 0.506400 | |
| 840 0.512500 | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.0002 | |
| - train_batch_size: 8 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 16 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: constant | |
| - lr_scheduler_warmup_ratio: 0.1 | |
| - num_epochs: 3 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.33.1 | |
| - Pytorch 2.0.1+cu118 | |
| - Datasets 2.14.5 | |
| - Tokenizers 0.13.3 | |