Text Generation
Transformers
Safetensors
GGUF
phi3
conversational
custom_code
text-generation-inference
Instructions to use SciPhi/Triplex with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SciPhi/Triplex with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SciPhi/Triplex", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SciPhi/Triplex", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("SciPhi/Triplex", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - llama-cpp-python
How to use SciPhi/Triplex with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="SciPhi/Triplex", filename="quantized_model-Q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use SciPhi/Triplex with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf SciPhi/Triplex:Q4_K_M # Run inference directly in the terminal: llama-cli -hf SciPhi/Triplex:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf SciPhi/Triplex:Q4_K_M # Run inference directly in the terminal: llama-cli -hf SciPhi/Triplex:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf SciPhi/Triplex:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf SciPhi/Triplex:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf SciPhi/Triplex:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf SciPhi/Triplex:Q4_K_M
Use Docker
docker model run hf.co/SciPhi/Triplex:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use SciPhi/Triplex with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SciPhi/Triplex" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SciPhi/Triplex", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/SciPhi/Triplex:Q4_K_M
- SGLang
How to use SciPhi/Triplex 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 "SciPhi/Triplex" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SciPhi/Triplex", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "SciPhi/Triplex" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SciPhi/Triplex", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use SciPhi/Triplex with Ollama:
ollama run hf.co/SciPhi/Triplex:Q4_K_M
- Unsloth Studio new
How to use SciPhi/Triplex with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for SciPhi/Triplex to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for SciPhi/Triplex to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for SciPhi/Triplex to start chatting
- Docker Model Runner
How to use SciPhi/Triplex with Docker Model Runner:
docker model run hf.co/SciPhi/Triplex:Q4_K_M
- Lemonade
How to use SciPhi/Triplex with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull SciPhi/Triplex:Q4_K_M
Run and chat with the model
lemonade run user.Triplex-Q4_K_M
List all available models
lemonade list
Upload Phi3ForCausalLM
Browse files- config.json +139 -0
- generation_config.json +11 -0
- model-00001-of-00002.safetensors +3 -0
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +202 -0
config.json
ADDED
|
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| 1 |
+
{
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| 2 |
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"_name_or_path": "/home/ec2-user/shreyas/ft/",
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| 3 |
+
"architectures": [
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| 4 |
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"Phi3ForCausalLM"
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| 5 |
+
],
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| 6 |
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"attention_bias": false,
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| 7 |
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"attention_dropout": 0.0,
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| 8 |
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"auto_map": {
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| 9 |
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"AutoConfig": "configuration_phi3.Phi3Config",
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| 10 |
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"AutoModel": "modeling_phi3.Phi3ForCausalLM",
|
| 11 |
+
"AutoModelForCausalLM": "microsoft/Phi-3-mini-128k-instruct--modeling_phi3.Phi3ForCausalLM"
|
| 12 |
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},
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| 13 |
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"bos_token_id": 1,
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| 14 |
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"embd_pdrop": 0.0,
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| 15 |
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"eos_token_id": 32000,
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| 16 |
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"hidden_act": "silu",
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"hidden_size": 3072,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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| 20 |
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"max_position_embeddings": 131072,
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| 21 |
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"model_type": "phi3",
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| 22 |
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"num_attention_heads": 32,
|
| 23 |
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"num_hidden_layers": 32,
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| 24 |
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"num_key_value_heads": 32,
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| 25 |
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"original_max_position_embeddings": 4096,
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| 26 |
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"pad_token_id": 32000,
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| 27 |
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"resid_pdrop": 0.0,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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| 30 |
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"long_factor": [
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| 31 |
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1.0700000524520874,
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62.91739273071289,
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],
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"short_factor": [
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1.1,
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2.1000000000000005,
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2.1000000000000005,
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2.1500000000000004,
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| 115 |
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2.25,
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| 116 |
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2.25,
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| 117 |
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2.25,
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2.25,
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| 119 |
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2.25,
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| 120 |
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2.3999999999999995,
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2.4499999999999993,
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2.499999999999999,
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| 123 |
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2.6999999999999984,
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| 124 |
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2.6999999999999984,
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2.7499999999999982,
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| 126 |
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2.8999999999999977,
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| 128 |
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3.049999999999997
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| 129 |
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],
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| 130 |
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"type": "longrope"
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| 131 |
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},
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| 132 |
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"rope_theta": 10000.0,
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| 133 |
+
"sliding_window": 262144,
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| 134 |
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"tie_word_embeddings": false,
|
| 135 |
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"torch_dtype": "bfloat16",
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| 136 |
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"transformers_version": "4.42.3",
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| 137 |
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"use_cache": false,
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| 138 |
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"vocab_size": 32064
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| 139 |
+
}
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generation_config.json
ADDED
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{
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"_from_model_config": true,
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| 3 |
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"bos_token_id": 1,
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| 4 |
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"eos_token_id": [
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| 5 |
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32000,
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| 6 |
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32001,
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| 7 |
+
32007
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| 8 |
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],
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| 9 |
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"pad_token_id": 32000,
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| 10 |
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"transformers_version": "4.42.3"
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| 11 |
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}
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model-00001-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:243b23e2ba84c6088a77298451025e875e45a89118c26ff2a14644208cd338cb
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| 3 |
+
size 4972489328
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model-00002-of-00002.safetensors
ADDED
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:3921621dd4d3a28b5509596d4dfd51f72db53b1c478056e512df2114d655de7a
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| 3 |
+
size 2669692552
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model.safetensors.index.json
ADDED
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