Text Generation
Transformers
Safetensors
GGUF
PEFT
English
qwen2
agent
function-calling
tool-use
h-res
manifold-steering
uraion-labs
uraion
iclr-2026
associative-memory
hopfield
neural-collapse
qwen2.5
sft
trl
hermes-function-calling
apigen
xlam
toolace
conversational
text-generation-inference
Instructions to use UraionLabs/Uraion-Agent-Steer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UraionLabs/Uraion-Agent-Steer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="UraionLabs/Uraion-Agent-Steer") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("UraionLabs/Uraion-Agent-Steer") model = AutoModelForCausalLM.from_pretrained("UraionLabs/Uraion-Agent-Steer") 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]:])) - PEFT
How to use UraionLabs/Uraion-Agent-Steer with PEFT:
Task type is invalid.
- llama-cpp-python
How to use UraionLabs/Uraion-Agent-Steer with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="UraionLabs/Uraion-Agent-Steer", filename="Uraion-Agent-Steer-Q2_K.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use UraionLabs/Uraion-Agent-Steer with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf UraionLabs/Uraion-Agent-Steer:Q4_K_M # Run inference directly in the terminal: llama cli -hf UraionLabs/Uraion-Agent-Steer:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf UraionLabs/Uraion-Agent-Steer:Q4_K_M # Run inference directly in the terminal: llama cli -hf UraionLabs/Uraion-Agent-Steer: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 UraionLabs/Uraion-Agent-Steer:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf UraionLabs/Uraion-Agent-Steer: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 UraionLabs/Uraion-Agent-Steer:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf UraionLabs/Uraion-Agent-Steer:Q4_K_M
Use Docker
docker model run hf.co/UraionLabs/Uraion-Agent-Steer:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use UraionLabs/Uraion-Agent-Steer with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "UraionLabs/Uraion-Agent-Steer" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UraionLabs/Uraion-Agent-Steer", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/UraionLabs/Uraion-Agent-Steer:Q4_K_M
- SGLang
How to use UraionLabs/Uraion-Agent-Steer 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 "UraionLabs/Uraion-Agent-Steer" \ --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": "UraionLabs/Uraion-Agent-Steer", "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 "UraionLabs/Uraion-Agent-Steer" \ --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": "UraionLabs/Uraion-Agent-Steer", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use UraionLabs/Uraion-Agent-Steer with Ollama:
ollama run hf.co/UraionLabs/Uraion-Agent-Steer:Q4_K_M
- Unsloth Studio
How to use UraionLabs/Uraion-Agent-Steer 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 UraionLabs/Uraion-Agent-Steer 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 UraionLabs/Uraion-Agent-Steer to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for UraionLabs/Uraion-Agent-Steer to start chatting
- Pi
How to use UraionLabs/Uraion-Agent-Steer with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf UraionLabs/Uraion-Agent-Steer:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "UraionLabs/Uraion-Agent-Steer:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use UraionLabs/Uraion-Agent-Steer with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf UraionLabs/Uraion-Agent-Steer:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default UraionLabs/Uraion-Agent-Steer:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use UraionLabs/Uraion-Agent-Steer with Docker Model Runner:
docker model run hf.co/UraionLabs/Uraion-Agent-Steer:Q4_K_M
- Lemonade
How to use UraionLabs/Uraion-Agent-Steer with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull UraionLabs/Uraion-Agent-Steer:Q4_K_M
Run and chat with the model
lemonade run user.Uraion-Agent-Steer-Q4_K_M
List all available models
lemonade list
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- chat_template.jinja +54 -0
- config.json +61 -0
- generation_config.json +13 -0
- model.safetensors +3 -0
- tokenizer.json +3 -0
- tokenizer_config.json +30 -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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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
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{%- else %}
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{%- if messages[0]['role'] == 'system' %}
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{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{{- '<|im_start|>user' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- message.content }}
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{{- '\n</tool_response>' }}
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{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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{%- endif %}
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{%- endif %}
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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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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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| 6 |
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"bos_token_id": null,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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| 43 |
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"max_position_embeddings": 32768,
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| 44 |
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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| 47 |
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"num_hidden_layers": 28,
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| 48 |
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"num_key_value_heads": 4,
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| 49 |
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"pad_token_id": 151643,
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| 50 |
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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| 56 |
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"tie_word_embeddings": false,
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| 57 |
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"transformers_version": "5.12.0",
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| 58 |
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"use_cache": false,
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| 59 |
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"use_sliding_window": false,
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"vocab_size": 152064
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}
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generation_config.json
ADDED
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{
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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| 8 |
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"repetition_penalty": 1.05,
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| 9 |
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"temperature": 0.7,
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| 10 |
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"top_k": 20,
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| 11 |
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"top_p": 0.8,
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| 12 |
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"transformers_version": "5.12.0"
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}
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model.safetensors
ADDED
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version https://git-lfs.github.com/spec/v1
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oid sha256:1418cf49be2f6661984386da2136e9de4cb510ae2c80aa16c267a046ea8fffb6
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| 3 |
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size 15256971232
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tokenizer.json
ADDED
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:3fd169731d2cbde95e10bf356d66d5997fd885dd8dbb6fb4684da3f23b2585d8
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size 11421892
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tokenizer_config.json
ADDED
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{
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"add_prefix_space": false,
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"backend": "tokenizers",
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| 4 |
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"bos_token": null,
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| 5 |
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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| 7 |
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"errors": "replace",
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| 8 |
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"extra_special_tokens": [
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"<|im_start|>",
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"<|im_end|>",
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"<|object_ref_start|>",
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"<|object_ref_end|>",
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"<|box_start|>",
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"<|box_end|>",
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| 15 |
+
"<|quad_start|>",
|
| 16 |
+
"<|quad_end|>",
|
| 17 |
+
"<|vision_start|>",
|
| 18 |
+
"<|vision_end|>",
|
| 19 |
+
"<|vision_pad|>",
|
| 20 |
+
"<|image_pad|>",
|
| 21 |
+
"<|video_pad|>"
|
| 22 |
+
],
|
| 23 |
+
"is_local": false,
|
| 24 |
+
"local_files_only": false,
|
| 25 |
+
"model_max_length": 131072,
|
| 26 |
+
"pad_token": "<|endoftext|>",
|
| 27 |
+
"split_special_tokens": false,
|
| 28 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
| 29 |
+
"unk_token": null
|
| 30 |
+
}
|