Text Generation
Transformers
Safetensors
jirack_ternary
ternary
bitnet
1.58bit
llama
ternary-transformer
quantized
70b
jirack
amd-rocm
vram-optimized
Eval Results (legacy)
2-bit
Instructions to use kgrabko/JiRackTernary_70b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kgrabko/JiRackTernary_70b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kgrabko/JiRackTernary_70b")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("kgrabko/JiRackTernary_70b", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use kgrabko/JiRackTernary_70b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kgrabko/JiRackTernary_70b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kgrabko/JiRackTernary_70b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/kgrabko/JiRackTernary_70b
- SGLang
How to use kgrabko/JiRackTernary_70b 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 "kgrabko/JiRackTernary_70b" \ --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": "kgrabko/JiRackTernary_70b", "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 "kgrabko/JiRackTernary_70b" \ --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": "kgrabko/JiRackTernary_70b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use kgrabko/JiRackTernary_70b with Docker Model Runner:
docker model run hf.co/kgrabko/JiRackTernary_70b
Ctrl+K
- ALFA_VERSION
- Magpie-Pro-MT-100K
- Magpie-Pro-MT-300K-filtered
- No_robots
- SlimOrca
- checkpoints
- merge
- prepared_sft_data
- sftdata_alpaca
- tokenized_base_dataset_en_chunks
- wikipedia_dataset
- 6.02 kB
- 6.84 kB
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- 6.41 kB
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- 316 Bytes
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- 2.96 kB
- 14.2 kB
- 1.77 GB xet
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- 2.41 GB xet
- 583 MB xet
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