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Model: sahilchachra/MiniCPM5-1B-Uncensored Source: Original Platform
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README.md
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README.md
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---
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base_model: openbmb/MiniCPM5-1B
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language:
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- en
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- zh
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license: other
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tags:
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- uncensored
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- abliteration
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- safety-research
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- mlx
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- reasoning
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- minicpm
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pipeline_tag: text-generation
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---
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# MiniCPM5-1B — Uncensored
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A fully uncensored version of [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B), produced with a single training-free stage: **single-direction abliteration** (Arditi et al., 2024). Refusals on AdvBench drop from **85% → 2%** with **zero over-refusal regression** on benign prompts — no fine-tuning, no new data, weights edited directly.
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> **Intended for:** security research, red-teaming, jailbreak benchmarking, and AI-safety study. Not intended for production deployment or harmful use.
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---
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## Benchmark Results
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Evaluated on [AdvBench](https://github.com/llm-attacks/llm-attacks) (100 harmful behaviors) and an over-refusal set (40 benign prompts). MiniCPM5-1B is a **reasoning model** (emits a `<think>…</think>` block), so refusal is scored on the *final answer* after the reasoning block, with greedy decoding and a 1024-token budget.
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### Harmful prompt refusal rate ↓ lower is more uncensored
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| Model | Refused / 100 | Refusal Rate |
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|---|---|---|
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| MiniCPM5-1B (original) | 85 / 100 | 85.0% |
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| **MiniCPM5-1B-Uncensored (this model)** | **2 / 100** | **2.0%** |
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### Over-refusal rate on benign prompts ↓ lower is better
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| Model | Refused / 40 | Refusal Rate |
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|---|---|---|
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| MiniCPM5-1B (original) | 0 / 40 | 0.0% |
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| **MiniCPM5-1B-Uncensored (this model)** | **0 / 40** | **0.0%** |
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A **83-point** drop in harmful refusals while preserving benign behavior.
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---
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## Pipeline — Single-Direction Abliteration (training-free)
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Based on [Arditi et al., *"Refusal in LLMs Is Mediated by a Single Direction"* (2024)](https://arxiv.org/abs/2406.11717). Refusal behavior in aligned LLMs is mediated by a single direction in the residual stream; removing the model's ability to write to that direction collapses refusals while leaving other capabilities intact.
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1. **Collect activations.** Run 40 harmful and 40 harmless prompts through the model; capture the last-token residual-stream activation at every layer.
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2. **Compute candidate directions.** Per layer: `r = normalize(mean_harmful − mean_harmless)`.
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3. **Select the single best direction.** Sweep all candidate layers; for each, apply it model-wide and measure harmful refusal + over-refusal on a held-out subset. **Layer 12** scored best (0% harmful / 0% over-refusal on the eval subset).
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4. **Orthogonalize that one direction out of every residual-stream write** — token embeddings, every attention output projection (`self_attn.o_proj`), and every MLP down-projection (`mlp.down_proj`):
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```
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W_new = W − r · (rᵀ W) # for residual-stream writers
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E_new = E − (E r) · rᵀ # for token embeddings
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```
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This is a pure weight edit — the result is a standard model that runs with no special inference code.
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> **Why a single direction?** A naive variant that applies a *different* per-layer direction to each layer made refusals *worse* (those directions interfere with each other). Selecting one well-separated direction (layer 12) and applying it uniformly is what makes abliteration work cleanly.
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---
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## Model Details
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| Property | Value |
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|---|---|
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| Base model | openbmb/MiniCPM5-1B |
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| Architecture | Llama-style transformer (GQA) |
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| Parameters | ~1.0B |
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| Layers | 24 |
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| Hidden size | 1536 |
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| Attention | 16 heads / 2 KV heads (GQA), head dim 128 |
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| Intermediate size | 4608 |
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| Vocab | 130,560 |
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| Context | 131K tokens |
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| Reasoning | Emits `<think>…</think>` before the final answer |
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| Format | MLX bfloat16 safetensors |
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---
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## Usage (MLX)
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```python
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from mlx_lm import load, generate
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from mlx_lm.sample_utils import make_sampler, make_logits_processors
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model, tokenizer = load("sahilchachra/MiniCPM5-1B-Uncensored")
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messages = [{"role": "user", "content": "Your prompt here"}]
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prompt = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True, tokenize=False
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)
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response = generate(
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model, tokenizer,
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prompt=prompt,
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max_tokens=1024,
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sampler=make_sampler(temp=0.0),
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logits_processors=make_logits_processors(repetition_penalty=1.05),
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)
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print(response)
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```
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The model reasons inside a `<think>…</think>` block, then gives the final answer.
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---
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## Limitations & Warnings
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- **Abliteration is surgical, not lossless** — removing the refusal direction can occasionally affect responses that legitimately overlap with it. General reasoning and benign behavior are preserved (0% over-refusal on the benign set).
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- **No new knowledge** — abliteration only removes refusal behavior; it adds no information or capability.
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- **Small model** — at ~1B parameters, factual accuracy and complex reasoning are limited regardless of alignment.
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- **Responsible use** — published for safety research and red-teaming. The authors do not endorse harmful use of this model.
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---
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## Citation
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```bibtex
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@article{arditi2024refusal,
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title={Refusal in Language Models Is Mediated by a Single Direction},
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author={Arditi, Andy and Obeso, Oscar and Syed, Aaquib and Steinhardt, Jacob and Nanda, Neel and Heimersheim, Stefan},
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journal={arXiv preprint arXiv:2406.11717},
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year={2024}
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}
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```
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---
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*Created with [UncensorLLMs](https://github.com/sahilchachra/UncensorLLMs)*
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179
chat_template.jinja
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chat_template.jinja
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{{- bos_token }}{%- if tools %}
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{%- set tool_definitions %}
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{{- "# Tools\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(ensure_ascii=False) }}
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{%- endfor %}
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{{- '\n</tools>\n\nTool usage guidelines:\n- You may call zero or more functions. If no function calls are needed, just answer normally and do not include any <function ... </function>.\n- When calling a function, return an XML object within <function ... </function> using:\n<function name="function-name"><param name="param-name">param-value</param></function>\n- param-value may be multi-line. If it contains <, & or newline characters, wrap it in a CDATA block: <param name="param-name"><![CDATA[...multi-line value...]]></param>' }}
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{%- endset %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0].role == 'system' %}
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{%- if '<tool_def_sep>' in messages[0].content %}
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{{- messages[0].content.replace('<tool_def_sep>', tool_definitions) }}
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{%- else %}
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{{- messages[0].content + '\n\n' + tool_definitions }}
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{%- endif %}
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{%- else %}
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{{- tool_definitions.lstrip() }}
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{%- endif %}
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{{- '<|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 message.tool_calls %}
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{%- set content_parts = content.split('<tool_sep>') %}
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{%- set processed_content = content_parts[0] %}
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{%- set tool_calls_count = message.tool_calls|length %}
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{%- set tool_sep_count = content_parts|length - 1 %}
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{%- set min_count = [tool_calls_count, tool_sep_count]|min %}
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{%- for i in range(1, content_parts|length) %}
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{%- set tool_index = i - 1 %}
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{%- if tool_index < tool_calls_count %}
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{%- set tool_call = message.tool_calls[tool_index] %}
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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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{%- set single_tool_xml %}
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{{- '<function name="' ~ tool_call.name ~ '">' }}
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{%- if tool_call.arguments %}
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{%- set args_dict = tool_call.arguments %}
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{%- for param_name, param_value in args_dict.items() %}
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{{- '<param name="' ~ param_name ~ '">' }}
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{%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
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{{- '<![CDATA[' + param_value + ']]>' }}
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{%- else %}
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{{- param_value }}
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{%- endif %}
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{{- '</param>' }}
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{%- endfor %}
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{%- endif %}
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{{- '</function>' }}
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{%- endset %}
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{%- set processed_content = processed_content + single_tool_xml + content_parts[i] %}
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{%- else %}
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{%- set processed_content = processed_content + content_parts[i] %}
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{%- endif %}
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{%- endfor %}
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|
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{%- if tool_calls_count > tool_sep_count %}
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{%- for remaining_index in range(tool_sep_count, tool_calls_count) %}
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{%- set tool_call = message.tool_calls[remaining_index] %}
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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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{%- set remaining_tool_xml %}
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{{- '<function name="' ~ tool_call.name ~ '">' }}
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{%- if tool_call.arguments %}
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{%- set args_dict = tool_call.arguments %}
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{%- for param_name, param_value in args_dict.items() %}
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{{- '<param name="' ~ param_name ~ '">' }}
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{%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
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{{- '<![CDATA[' + param_value + ']]>' }}
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{%- else %}
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{{- param_value }}
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{%- endif %}
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{{- '</param>' }}
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{%- endfor %}
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{%- endif %}
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{{- '</function>' }}
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{%- endset %}
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{%- set processed_content = processed_content + remaining_tool_xml %}
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{%- endfor %}
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{%- endif %}
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{%- set content = processed_content %}
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{%- endif %}
|
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{%- if loop.index0 > ns.last_query_index %}
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{%- if 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 }}
|
||||
{%- endif %}
|
||||
|
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{%- if message.tool_calls and not has_tool_sep %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
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{{- '\n' }}
|
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{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<function name="' ~ tool_call.name ~ '">' }}
|
||||
{%- if tool_call.arguments %}
|
||||
{%- set args_dict = tool_call.arguments %}
|
||||
{%- for param_name, param_value in args_dict.items() %}
|
||||
{{- '<param name="' ~ param_name ~ '">' }}
|
||||
{%- if param_value is string and ('<' in param_value or '&' in param_value or '\n' in param_value) %}
|
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{{- '<![CDATA[' + param_value + ']]>' }}
|
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{%- else %}
|
||||
{{- param_value }}
|
||||
{%- endif %}
|
||||
{{- '</param>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '</function>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|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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{%- if message.content is string %}
|
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{{- content }}
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{%- else %}
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{{- message.content | tojson(ensure_ascii=False) }}
|
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{%- endif %}
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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' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
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{%- if add_generation_prompt %}
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{{- '<|im_start|>assistant\n' }}
|
||||
{%- if enable_thinking is defined %}
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{%- if enable_thinking is false %}
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{{- '<think>\n\n</think>\n\n' }}
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||||
{%- elif enable_thinking is true %}
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{{- '<think>\n' }}
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{%- endif %}
|
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{%- endif %}
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{%- endif %}
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30
config.json
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config.json
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{
|
||||
"_name_or_path": "openbmb/MiniCPM5-1B",
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"architectures": [
|
||||
"LlamaForCausalLM"
|
||||
],
|
||||
"bos_token_id": 0,
|
||||
"eos_token_id": [
|
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1,
|
||||
130073
|
||||
],
|
||||
"pad_token_id": 1,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 1536,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 4608,
|
||||
"max_position_embeddings": 131072,
|
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"model_type": "llama",
|
||||
"num_attention_heads": 16,
|
||||
"num_hidden_layers": 24,
|
||||
"num_key_value_heads": 2,
|
||||
"head_dim": 128,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_theta": 5000000,
|
||||
"rope_scaling": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "5.6.2",
|
||||
"use_cache": true,
|
||||
"vocab_size": 130560
|
||||
}
|
||||
13
generation_config.json
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13
generation_config.json
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||||
{
|
||||
"_from_model_config": true,
|
||||
"bos_token_id": 0,
|
||||
"eos_token_id": [
|
||||
1,
|
||||
130073
|
||||
],
|
||||
"pad_token_id": 1,
|
||||
"do_sample": true,
|
||||
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||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
653947
tokenizer.json
Normal file
653947
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
4099
tokenizer_config.json
Normal file
4099
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user