Safetensors
GGUF
qwen3
cybersecurity
agentic
red-team
blue-team
distillation
glm-5.2
kimi-k3
function-calling
tool-calling
tool-use
conversational
Instructions to use bebrws/security-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use bebrws/security-8b 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 bebrws/security-8b:Q4_K_M # Run inference directly in the terminal: llama cli -hf bebrws/security-8b:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf bebrws/security-8b:Q4_K_M # Run inference directly in the terminal: llama cli -hf bebrws/security-8b: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 bebrws/security-8b:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bebrws/security-8b: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 bebrws/security-8b:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bebrws/security-8b:Q4_K_M
Use Docker
docker model run hf.co/bebrws/security-8b:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use bebrws/security-8b with Ollama:
ollama run hf.co/bebrws/security-8b:Q4_K_M
- Unsloth Studio
How to use bebrws/security-8b 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 bebrws/security-8b 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 bebrws/security-8b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for bebrws/security-8b to start chatting
- Pi
How to use bebrws/security-8b with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bebrws/security-8b: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": "bebrws/security-8b:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use bebrws/security-8b with Docker Model Runner:
docker model run hf.co/bebrws/security-8b:Q4_K_M
- Lemonade
How to use bebrws/security-8b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bebrws/security-8b:Q4_K_M
Run and chat with the model
lemonade run user.security-8b-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use bebrws/security-8b with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bebrws/security-8b: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 bebrws/security-8b:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use bebrws/security-8b with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf bebrws/security-8b:Q4_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "bebrws/security-8b:Q4_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
main <- v7cti weights (r7-CTI round: format-matched CTI SFT)
Browse files- chat_template.jinja +89 -0
- config.json +76 -0
- generation_config.json +12 -0
- model.safetensors +3 -0
chat_template.jinja
ADDED
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| 1 |
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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 + '\n\n' }}
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{%- endif %}
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{{- "# 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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{%- endif %}
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{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
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{%- set index = (messages|length - 1) - loop.index0 %}
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- set ns.multi_step_tool = false %}
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{%- set ns.last_query_index = index %}
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{%- endif %}
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{%- endfor %}
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{%- for message in messages %}
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{%- if message.content is string %}
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{%- set content = message.content %}
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{%- else %}
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{%- set content = '' %}
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{%- endif %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{%- set reasoning_content = '' %}
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{%- if message.reasoning_content is string %}
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{%- set reasoning_content = message.reasoning_content %}
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{%- else %}
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{%- if '</think>' in content %}
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- if loop.index0 > ns.last_query_index %}
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{%- if loop.last or (not loop.last and reasoning_content) %}
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- else %}
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{{- '<|im_start|>' + message.role + '\n' + content }}
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{%- endif %}
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{%- if message.tool_calls %}
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{%- for tool_call in message.tool_calls %}
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{%- if (loop.first and content) or (not loop.first) %}
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{{- '\n' }}
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{%- endif %}
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{%- if tool_call.function %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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{{- '<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
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{{- '", "arguments": ' }}
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{%- if tool_call.arguments is string %}
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{{- tool_call.arguments }}
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{%- else %}
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{{- tool_call.arguments | tojson }}
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{%- endif %}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
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{%- endif %}
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{{- '<|im_end|>\n' }}
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{%- elif message.role == "tool" %}
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{%- if loop.first 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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{{- 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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{%- if enable_thinking is defined and enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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{%- endif %}
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{%- endif %}
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config.json
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{
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| 2 |
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"architectures": [
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| 3 |
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"Qwen3ForCausalLM"
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],
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| 5 |
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"attention_bias": false,
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| 6 |
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"attention_dropout": 0.0,
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| 7 |
+
"bos_token_id": null,
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| 8 |
+
"dtype": "bfloat16",
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| 9 |
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"eos_token_id": 151645,
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| 10 |
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"head_dim": 128,
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| 11 |
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"hidden_act": "silu",
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| 12 |
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"hidden_size": 4096,
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| 13 |
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"initializer_range": 0.02,
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| 14 |
+
"intermediate_size": 12288,
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| 15 |
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"layer_types": [
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| 16 |
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"full_attention",
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| 17 |
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"full_attention",
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| 18 |
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"full_attention",
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| 19 |
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"full_attention",
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| 20 |
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"full_attention",
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| 21 |
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"full_attention",
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| 22 |
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"full_attention",
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| 23 |
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"full_attention",
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| 24 |
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"full_attention",
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| 25 |
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"full_attention",
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| 26 |
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"full_attention",
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| 27 |
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"full_attention",
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| 28 |
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"full_attention",
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| 29 |
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"full_attention",
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| 30 |
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"full_attention",
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| 31 |
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"full_attention",
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| 32 |
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"full_attention",
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| 33 |
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"full_attention",
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| 34 |
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"full_attention",
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| 35 |
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"full_attention",
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| 36 |
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"full_attention",
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| 37 |
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"full_attention",
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| 38 |
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"full_attention",
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| 39 |
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"full_attention",
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| 40 |
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"full_attention",
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| 41 |
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"full_attention",
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| 42 |
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"full_attention",
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| 43 |
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"full_attention",
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| 44 |
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"full_attention",
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| 45 |
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"full_attention",
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| 46 |
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"full_attention",
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| 47 |
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"full_attention",
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| 48 |
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"full_attention",
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| 49 |
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"full_attention",
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| 50 |
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"full_attention",
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| 51 |
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"full_attention"
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| 52 |
+
],
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| 53 |
+
"max_position_embeddings": 131072,
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| 54 |
+
"max_window_layers": 36,
|
| 55 |
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"model_type": "qwen3",
|
| 56 |
+
"num_attention_heads": 32,
|
| 57 |
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"num_hidden_layers": 36,
|
| 58 |
+
"num_key_value_heads": 8,
|
| 59 |
+
"pad_token_id": 151643,
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| 60 |
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"rms_norm_eps": 1e-06,
|
| 61 |
+
"rope_parameters": {
|
| 62 |
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"rope_theta": 1000000,
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| 63 |
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"rope_type": "default"
|
| 64 |
+
},
|
| 65 |
+
"sliding_window": null,
|
| 66 |
+
"tie_word_embeddings": false,
|
| 67 |
+
"transformers_version": "5.14.1",
|
| 68 |
+
"use_cache": false,
|
| 69 |
+
"use_sliding_window": false,
|
| 70 |
+
"vocab_size": 151936,
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| 71 |
+
"rope_scaling": {
|
| 72 |
+
"rope_type": "yarn",
|
| 73 |
+
"factor": 4.0,
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| 74 |
+
"original_max_position_embeddings": 32768
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| 75 |
+
}
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| 76 |
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}
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generation_config.json
ADDED
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{
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| 2 |
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"do_sample": true,
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| 3 |
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"eos_token_id": [
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| 4 |
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151645,
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| 5 |
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151643
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| 6 |
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],
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| 7 |
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"pad_token_id": 151643,
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| 8 |
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"temperature": 0.6,
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| 9 |
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"top_k": 20,
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| 10 |
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"top_p": 0.95,
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| 11 |
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"transformers_version": "5.14.1"
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| 12 |
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:e3a924815c0bfa909593fbecc0f7fb6e0e8d3b9361bae4405474e2145ec4fb1b
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| 3 |
+
size 16381517208
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