Instructions to use Rexopia/HawkLM-demo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rexopia/HawkLM-demo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Rexopia/HawkLM-demo", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Rexopia/HawkLM-demo", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Rexopia/HawkLM-demo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Rexopia/HawkLM-demo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Rexopia/HawkLM-demo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Rexopia/HawkLM-demo
- SGLang
How to use Rexopia/HawkLM-demo 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 "Rexopia/HawkLM-demo" \ --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": "Rexopia/HawkLM-demo", "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 "Rexopia/HawkLM-demo" \ --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": "Rexopia/HawkLM-demo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Rexopia/HawkLM-demo with Docker Model Runner:
docker model run hf.co/Rexopia/HawkLM-demo
update new info
Browse files- README.md +19 -17
- config.json +1 -1
- tokenizer_config.json +6 -6
README.md
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license: apache-2.0
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datasets:
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language:
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pipeline_tag: text-generation
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#
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<!-- Provide a quick summary of what the model is/does. -->
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- **Developed by:**
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources
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<!-- Provide the basic links for the model. -->
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- **Repository:**
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- **Demo [optional]:** [More Information Needed]
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## Uses
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Use the code below to get started with the model.
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## Training Details
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<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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### Training Procedure
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license: apache-2.0
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language:
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- en
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tags:
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- hawk
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pipeline_tag: text-generation
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# Hawk-demo
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<!-- Provide a quick summary of what the model is/does. -->
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- **Developed by:** Rexopia
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- **Reach me:** ruiji.zhang@outlook.com
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- **Language(s) (NLP):** English
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- **License:** Apache license 2.0
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- **Pretrained model [optional]:** True
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### Model Sources
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<!-- Provide the basic links for the model. -->
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- **Github Repository:** Coming soon
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- **Demo version:** True
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## Uses
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Use the code below to get started with the model.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("Rexopia/HawkLM-demo", trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained("Rexopia/HawkLM-demo", device_map="auto", trust_remote_code=True)
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```
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## Training Details
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<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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We sampled from Redpajama 1T datasets without any Arxiv and GitHub tags.
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### Training Procedure
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config.json
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"embd_pdrop": 0.0,
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"eos_token_id": 65535,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-
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"model_type": "hawk",
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"n_embd": 1024,
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"n_head": 16,
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"embd_pdrop": 0.0,
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"eos_token_id": 65535,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-06,
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"model_type": "hawk",
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"n_embd": 1024,
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"n_head": 16,
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tokenizer_config.json
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"add_bos_token": false,
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"bos_token": {
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"content": "<s>",
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"normalized": true,
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"rstrip": false,
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"single_word": false
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},
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"auto_map": {
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"AutoTokenizer": [
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"add_bos_token": false,
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"auto_map": {
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"AutoTokenizer": [
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},
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"bos_token": {
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"content": "<s>",
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"normalized": true,
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"rstrip": false,
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"single_word": false
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}
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