Text Generation
Transformers
ONNX
Transformers.js
lfm2
emoji
thinking
chain-of-thought
pantheon
unsloth
lfm
webgpu
conversational
Instructions to use shreyask/pantheon-ui-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shreyask/pantheon-ui-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="shreyask/pantheon-ui-onnx") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("shreyask/pantheon-ui-onnx") model = AutoModelForCausalLM.from_pretrained("shreyask/pantheon-ui-onnx", device_map="auto") 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]:])) - Transformers.js
How to use shreyask/pantheon-ui-onnx with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('text-generation', 'shreyask/pantheon-ui-onnx'); - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use shreyask/pantheon-ui-onnx with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "shreyask/pantheon-ui-onnx" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "shreyask/pantheon-ui-onnx", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/shreyask/pantheon-ui-onnx
- SGLang
How to use shreyask/pantheon-ui-onnx 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 "shreyask/pantheon-ui-onnx" \ --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": "shreyask/pantheon-ui-onnx", "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 "shreyask/pantheon-ui-onnx" \ --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": "shreyask/pantheon-ui-onnx", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use shreyask/pantheon-ui-onnx 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 shreyask/pantheon-ui-onnx 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 shreyask/pantheon-ui-onnx to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for shreyask/pantheon-ui-onnx to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="shreyask/pantheon-ui-onnx", max_seq_length=2048, ) - Docker Model Runner
How to use shreyask/pantheon-ui-onnx with Docker Model Runner:
docker model run hf.co/shreyask/pantheon-ui-onnx
Upload ONNX int4 model via Xenova's LFM2 builder
Browse files- .gitattributes +1 -0
- chat_template.jinja +45 -0
- genai_config.json +49 -0
- model.onnx +3 -0
- model.onnx.data +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +20 -0
.gitattributes
CHANGED
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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model.onnx.data filter=lfs diff=lfs merge=lfs -text
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chat_template.jinja
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{{- bos_token -}}
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{%- set keep_past_thinking = keep_past_thinking | default(false) -%}
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{%- set ns = namespace(system_prompt="") -%}
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{%- if messages[0]["role"] == "system" -%}
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{%- set ns.system_prompt = messages[0]["content"] -%}
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{%- set messages = messages[1:] -%}
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{%- endif -%}
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{%- if tools -%}
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{%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%}
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{%- for tool in tools -%}
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{%- if tool is not string -%}
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{%- set tool = tool | tojson -%}
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{%- endif -%}
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{%- set ns.system_prompt = ns.system_prompt + tool -%}
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{%- if not loop.last -%}
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{%- set ns.system_prompt = ns.system_prompt + ", " -%}
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{%- endif -%}
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{%- endfor -%}
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{%- set ns.system_prompt = ns.system_prompt + "]" -%}
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{%- endif -%}
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{%- if ns.system_prompt -%}
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{{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
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{%- endif -%}
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{%- set ns.last_assistant_index = -1 -%}
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{%- for message in messages -%}
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{%- if message["role"] == "assistant" -%}
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{%- set ns.last_assistant_index = loop.index0 -%}
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{%- endif -%}
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{%- endfor -%}
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{%- for message in messages -%}
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{{- "<|im_start|>" + message["role"] + "\n" -}}
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{%- set content = message["content"] -%}
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{%- if content is not string -%}
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{%- set content = content | tojson -%}
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{%- endif -%}
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{%- if message["role"] == "assistant" and not keep_past_thinking and loop.index0 != ns.last_assistant_index -%}
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{%- if "</think>" in content -%}
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{%- set content = content.split("</think>")[-1] | trim -%}
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{%- endif -%}
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{%- endif -%}
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{{- content + "<|im_end|>\n" -}}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{- "<|im_start|>assistant\n" -}}
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{%- endif -%}
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genai_config.json
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{
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"model": {
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"bos_token_id": 1,
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"context_length": 128000,
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"decoder": {
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"session_options": {
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"log_id": "onnxruntime-genai",
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"provider_options": []
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},
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"filename": "model.onnx",
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"head_size": 64,
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"hidden_size": 2048,
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"inputs": {
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"input_ids": "input_ids",
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"attention_mask": "attention_mask",
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"past_key_names": "past_key_values.%d.key",
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"past_value_names": "past_key_values.%d.value"
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},
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"outputs": {
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"logits": "logits",
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"present_key_names": "present.%d.key",
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"present_value_names": "present.%d.value"
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},
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"num_attention_heads": 32,
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"num_hidden_layers": 16,
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"num_key_value_heads": 8
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},
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"eos_token_id": 7,
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"pad_token_id": 0,
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"type": "lfm2",
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"vocab_size": 65536
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},
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"search": {
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"diversity_penalty": 0.0,
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"do_sample": false,
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"early_stopping": true,
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| 37 |
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"length_penalty": 1.0,
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| 38 |
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"max_length": 128000,
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| 39 |
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"min_length": 0,
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| 40 |
+
"no_repeat_ngram_size": 0,
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| 41 |
+
"num_beams": 1,
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| 42 |
+
"num_return_sequences": 1,
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| 43 |
+
"past_present_share_buffer": true,
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| 44 |
+
"repetition_penalty": 1.0,
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| 45 |
+
"temperature": 1.0,
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| 46 |
+
"top_k": 50,
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| 47 |
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"top_p": 1.0
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| 48 |
+
}
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}
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model.onnx
ADDED
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:570efe13c7ebd2bd47fd8b81bda9f5ebb0a325e9c4f8dd9b5cf34d854f575ac3
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+
size 171707
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model.onnx.data
ADDED
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@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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+
oid sha256:235416e25ccadffa58b69d9f4db2fb3fe2a3e3c5370d5100bf3ae0f0e4a30afb
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+
size 1301544960
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tokenizer.json
ADDED
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The diff for this file is too large to render.
See raw diff
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tokenizer_config.json
ADDED
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{
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| 2 |
+
"backend": "tokenizers",
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| 3 |
+
"bos_token": "<|startoftext|>",
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| 4 |
+
"clean_up_tokenization_spaces": false,
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| 5 |
+
"eos_token": "<|im_end|>",
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| 6 |
+
"is_local": false,
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| 7 |
+
"legacy": false,
|
| 8 |
+
"model_input_names": [
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| 9 |
+
"input_ids",
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| 10 |
+
"attention_mask"
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| 11 |
+
],
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| 12 |
+
"model_max_length": 128000,
|
| 13 |
+
"pad_token": "<|pad|>",
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| 14 |
+
"padding_side": "left",
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| 15 |
+
"sp_model_kwargs": {},
|
| 16 |
+
"spaces_between_special_tokens": false,
|
| 17 |
+
"tokenizer_class": "TokenizersBackend",
|
| 18 |
+
"use_default_system_prompt": false,
|
| 19 |
+
"use_fast": true
|
| 20 |
+
}
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