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Model: ermiaazarkhalili/MiniCPM5-1B-SFT-Fable5 Source: Original Platform
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README.md
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---
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license: apache-2.0
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base_model:
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- openbmb/MiniCPM5-1B
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- unsloth
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- lora
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- trl
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- sft
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---
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# MiniCPM5-1B-SFT-Fable5
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A LoRA fine-tune of [`openbmb/MiniCPM5-1B`](https://huggingface.co/openbmb/MiniCPM5-1B), supervised fine-tuned on `ermiaazarkhalili/Fable-5-Complete-2M-Clean` (private).
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| | |
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| --- | --- |
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| **Base model** | [`openbmb/MiniCPM5-1B`](https://huggingface.co/openbmb/MiniCPM5-1B) |
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| **Architecture** | `LlamaForCausalLM` |
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| **Parameters** | 1.1B |
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| **Training data** | `ermiaazarkhalili/Fable-5-Complete-2M-Clean` (private) |
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| **Method** | LoRA supervised fine-tuning via [Unsloth](https://github.com/unslothai/unsloth) + [TRL](https://github.com/huggingface/trl) |
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| **License** | `apache-2.0` (inherited from the base model) |
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "ermiaazarkhalili/MiniCPM5-1B-SFT-Fable5"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, dtype='auto', device_map='auto')
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messages = [{"role": "user", "content": "Explain gradient checkpointing in two sentences."}]
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inputs = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True, return_tensors='pt'
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).to(model.device)
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outputs = model.generate(inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Training configuration
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| Setting | Value |
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| --- | --- |
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| LoRA rank (r) | 16 |
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| LoRA alpha | 16 |
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| Learning rate | 0.0002 |
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| Epochs | 1 |
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| Effective batch size | 8 (2 x 4 grad accum) |
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| Max sequence length | 4096 |
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| Base precision | 4-bit (QLoRA) |
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| Target modules | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`, `out_proj` |
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## Observed training loss
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Measured from our SLURM logs for this configuration. These are training-loss
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observations only — no downstream benchmark evaluation has been run on this
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model, so they should not be read as a quality claim.
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| SLURM job | Steps | First loss | Final loss |
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| --- | --- | --- | --- |
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| `53234830` | 47,128 | 1.5772 | 1.3098 |
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## Limitations
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- No benchmark evaluation has been run on this checkpoint. The only reported
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numbers are training-loss observations.
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- Inherits the biases, knowledge cutoff and failure modes of the base model.
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- Fine-tuned on a single instruction-following dataset; behaviour outside that
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distribution is untested.
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- LoRA adapters were merged into the base weights, so the merged model cannot
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be detached from this fine-tune.
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## Reproducing
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Trained by `notebooks/fable_distillation_minicpm5-1b_fable_unsloth.ipynb`, executed non-interactively with
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papermill on a SLURM H100 partition (Unsloth + TRL, LoRA).
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---
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*Card generated from the training run's own configuration and logs by*
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*`scripts/generate_hub_model_card.py`.*
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179
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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{%- 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 }}
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{%- endif %}
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{%- if message.tool_calls and not has_tool_sep %}
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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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||||||
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{{- '<function name="' ~ tool_call.name ~ '">' }}
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{%- if tool_call.arguments %}
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||||||
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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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{%- 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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{%- 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' }}
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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 %}
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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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32
config.json
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"torch_dtype": "bfloat16",
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"eos_token_id": 1,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 4608,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"num_key_value_heads": 2,
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"pad_token_id": 130559,
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"pretraining_tp": 1,
|
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|
"model.layers.4.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
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"model.layers.5.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
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"model.layers.6.input_layernorm.weight": "model-00000-of-00001.safetensors",
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||||||
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"model.layers.7.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.8.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.9.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.10.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.11.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.12.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.13.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.14.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.15.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.16.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.17.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.18.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.19.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.20.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
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|
||||||
|
"model.layers.22.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.23.input_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.0.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
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"model.layers.2.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
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||||||
|
"model.layers.3.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.4.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.5.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.6.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
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"model.layers.7.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.8.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.9.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.10.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.11.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.12.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
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|
||||||
|
"model.layers.14.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
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|
"model.layers.15.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.16.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.17.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.18.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.19.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.20.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.21.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.22.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.layers.23.post_attention_layernorm.weight": "model-00000-of-00001.safetensors",
|
||||||
|
"model.norm.weight": "model-00000-of-00001.safetensors"
|
||||||
|
}
|
||||||
|
}
|
||||||
653947
tokenizer.json
Normal file
653947
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
19
tokenizer_config.json
Normal file
19
tokenizer_config.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user