Text Generation
Transformers
TensorBoard
Safetensors
mistral
trl
dpo
Generated from Trainer
text-generation-inference
4-bit precision
gptq
Instructions to use thobuiq/teamtrack-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use thobuiq/teamtrack-ai with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="thobuiq/teamtrack-ai")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("thobuiq/teamtrack-ai") model = AutoModelForCausalLM.from_pretrained("thobuiq/teamtrack-ai", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use thobuiq/teamtrack-ai with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "thobuiq/teamtrack-ai" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thobuiq/teamtrack-ai", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/thobuiq/teamtrack-ai
- SGLang
How to use thobuiq/teamtrack-ai 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 "thobuiq/teamtrack-ai" \ --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": "thobuiq/teamtrack-ai", "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 "thobuiq/teamtrack-ai" \ --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": "thobuiq/teamtrack-ai", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use thobuiq/teamtrack-ai with Docker Model Runner:
docker model run hf.co/thobuiq/teamtrack-ai
Update config.json
Browse files- config.json +2 -2
config.json
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{
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"architectures": [
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"
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"bos_token_id": 1,
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"eos_token_id": 2,
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"initializer_range": 0.02,
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"intermediate_size": 28672,
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"max_position_embeddings": 8192,
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"model_type": "
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"num_attention_heads": 64,
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"num_hidden_layers": 80,
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"num_key_value_heads": 8,
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{
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"architectures": [
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"MistralForCausalLM"
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],
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"bos_token_id": 1,
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"eos_token_id": 2,
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"initializer_range": 0.02,
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"intermediate_size": 28672,
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"max_position_embeddings": 8192,
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"model_type": "mistral",
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"num_attention_heads": 64,
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"num_hidden_layers": 80,
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"num_key_value_heads": 8,
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