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Model: ermiaazarkhalili/Granite-4.1-8B-SFT-Fable5-Glint Source: Original Platform
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README.md
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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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- ibm-granite/granite-4.1-8b
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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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# Granite-4.1-8B-SFT-Fable5-Glint
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A LoRA fine-tune of [`ibm-granite/granite-4.1-8b`](https://huggingface.co/ibm-granite/granite-4.1-8b), supervised fine-tuned on `ermiaazarkhalili/Fable-5-Glint-Clean` (private).
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| | |
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| --- | --- |
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| **Base model** | [`ibm-granite/granite-4.1-8b`](https://huggingface.co/ibm-granite/granite-4.1-8b) |
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| **Architecture** | `GraniteForCausalLM` |
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| **Parameters** | 8.8B |
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| **Training data** | `ermiaazarkhalili/Fable-5-Glint-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/Granite-4.1-8B-SFT-Fable5-Glint"
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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 | 3 |
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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` |
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## Held-out evaluation
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Next-token accuracy on a deterministic held-out split of `ermiaazarkhalili/Fable-5-Glint-Clean` (n = 199 samples), scored against the base model [`ibm-granite/granite-4.1-8b`](https://huggingface.co/ibm-granite/granite-4.1-8b). Assistant tokens only; both models are scored identically.
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| Metric | Base | This model | Δ |
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| --- | ---: | ---: | ---: |
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| Top-1 accuracy | 0.5502 | 0.7258 | +0.1756 |
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| Top-5 accuracy | 0.8136 | 0.9305 | +0.1169 |
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A delta measures how far fine-tuning moved this model from its own starting point; it is not a ranking against other models, which start from different baselines.
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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 — see the held-out evaluation above for measured accuracy.
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| SLURM job | Steps | First loss | Final loss |
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| --- | --- | --- | --- |
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| `46020984` | 1,557 | 1.2249 | 0.6531 |
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| `45987985` | 900 | 1.3237 | 0.8099 |
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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_granite41-8b_fable-glint_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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114
chat_template.jinja
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{%- set tools_system_message_prefix = 'You are a helpful assistant with access to the following tools. You may call one or more tools to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>' %}
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{%- set tools_system_message_suffix = '\n</tools>\n\nFor each tool 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>. If a tool does not exist in the provided list of tools, notify the user that you do not have the ability to fulfill the request.' %}
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{%- set documents_system_message_prefix = 'You are a helpful assistant with access to the following documents. You may use one or more documents to assist with the user query.\n\nYou are given a list of documents within <documents></documents> XML tags:\n<documents>' %}
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{%- set documents_system_message_suffix = '\n</documents>\n\nWrite the response to the user\'s input by strictly aligning with the facts in the provided documents. If the information needed to answer the question is not available in the documents, inform the user that the question cannot be answered based on the available data.' %}
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{%- if available_tools is defined and available_tools %}
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{%- set tools = available_tools %}
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{%- endif %}
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{%- set ns = namespace(tools_system_message=tools_system_message_prefix,
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documents_system_message=documents_system_message_prefix,
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system_message=''
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) %}
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{%- if tools %}
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{%- for tool in tools %}
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{%- set ns.tools_system_message = ns.tools_system_message + '\n' + (tool | tojson) %}
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{%- endfor %}
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{%- set ns.tools_system_message = ns.tools_system_message + tools_system_message_suffix %}
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{%- else %}
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{%- set ns.tools_system_message = '' %}
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{%- endif %}
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{%- if documents %}
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{%- for document in documents %}
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{%- set ns.documents_system_message = ns.documents_system_message + '\n' + (document | tojson) %}
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{%- endfor %}
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{%- set ns.documents_system_message = ns.documents_system_message + documents_system_message_suffix %}
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{%- else %}
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{%- set ns.documents_system_message = '' %}
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{%- endif %}
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{%- if messages[0].role == 'system' %}
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{%- if messages[0].content is string %}
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{%- set ns.system_message = messages[0].content %}
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{%- elif messages[0].content is iterable %}
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{%- for entry in messages[0].content %}
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{%- if entry.type== 'text' %}
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{%- if ns.system_message != '' %}
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{%- set ns.system_message = ns.system_message + '\n' %}
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{%- endif %}
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{%- set ns.system_message = ns.system_message + entry.text %}
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{%- endif %}
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{%- endfor %}
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{%- endif %}
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{%- if tools and documents %}
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{%- set ns.system_message = ns.system_message + '\n\n' + ns.tools_system_message + '\n\n' + ns.documents_system_message %}
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{%- elif tools %}
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{%- set ns.system_message = ns.system_message + '\n\n' + ns.tools_system_message %}
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{%- elif documents %}
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{%- set ns.system_message = ns.system_message + '\n\n' + ns.documents_system_message %}
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{%- endif %}
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{%- else %}
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{%- if tools and documents %}
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{%- set ns.system_message = ns.tools_system_message + '\n\n' + ns.documents_system_message %}
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{%- elif tools %}
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{%- set ns.system_message = ns.tools_system_message %}
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{%- elif documents %}
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{%- set ns.system_message = ns.documents_system_message %}
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{%- endif %}
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{%- endif %}
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{%- if ns.system_message %}
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{{- '<|start_of_role|>system<|end_of_role|>' + ns.system_message + '<|end_of_text|>\n' }}
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{%- endif %}
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{%- for message in messages %}
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{%- set content = namespace(val='') %}
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{%- if message.content is string %}
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{%- set content.val = message.content %}
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{%- else %}
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{%- if message.content is iterable %}
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{%- for entry in message.content %}
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{%- if entry.type== 'text' %}
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{%- if content.val != '' %}
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{%- set content.val = content.val + '\n' %}
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{%- endif %}
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{%- set content.val = content.val + entry.text %}
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{%- endif %}
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{%- endfor %}
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{%- endif %}
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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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{{- '<|start_of_role|>' + message.role + '<|end_of_role|>' + content.val + '<|end_of_text|>\n' }}
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{%- elif message.role == 'assistant' %}
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{{- '<|start_of_role|>' + message.role + '<|end_of_role|>' + content.val }}
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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.val) 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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{{- '<|end_of_text|>\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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{{- '<|start_of_role|>user<|end_of_role|>' }}
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{%- endif %}
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{{- '\n<tool_response>\n' }}
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{{- content.val }}
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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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{{- '<|end_of_text|>\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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{{- '<|start_of_role|>assistant<|end_of_role|>' }}
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{%- endif %}
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35
config.json
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config.json
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{
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"architectures": [
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"GraniteForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attention_multiplier": 0.0078125,
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"bos_token_id": 100257,
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"torch_dtype": "bfloat16",
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"embedding_multiplier": 12.0,
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"eos_token_id": 100257,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.1,
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"intermediate_size": 12800,
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"logits_scaling": 16.0,
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"max_position_embeddings": 131072,
|
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"mlp_bias": false,
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"model_name": "ibm-granite/granite-4.1-8b",
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"model_type": "granite",
|
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"num_attention_heads": 32,
|
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"num_hidden_layers": 40,
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"num_key_value_heads": 8,
|
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"pad_token_id": 100256,
|
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"residual_multiplier": 0.22,
|
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
|
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"rope_theta": 10000000,
|
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"rope_type": "default"
|
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},
|
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"tie_word_embeddings": true,
|
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"unsloth_version": "2026.4.6",
|
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"use_cache": false,
|
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"vocab_size": 100352
|
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}
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}
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||||
501276
tokenizer.json
Normal file
501276
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
15
tokenizer_config.json
Normal file
15
tokenizer_config.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user