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Model: aimeri/spoomplesmaxx-cardmaker-v1 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: ibm-granite/granite-4.1-8b-base
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base_model_relation: finetune
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datasets:
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- aimeri/st-characters-alpaca
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language:
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- en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- sillytavern
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- character-cards
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- character-card-generation
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- roleplay
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- granite
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- granite-4.1
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- unsloth
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- trl
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- sft
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- lora
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- conversational
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---
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# SpoomplesMaxx Card Maker V1
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A fine-tune of [`ibm-granite/granite-4.1-8b-base`](https://huggingface.co/ibm-granite/granite-4.1-8b-base) that turns a short, open-ended prompt into a complete [SillyTavern](https://github.com/SillyTavern/SillyTavern) character card. Give it a concept — an archetype, a name and a few constraints, or just a one-liner — and it generates a full V2/V3-style card (description, personality, scenario, first message, example messages, and sometimes a lorebook).
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## Model Details
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- **Developed by:** [aimeri](https://huggingface.co/aimeri)
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- **Base model:** [`ibm-granite/granite-4.1-8b-base`](https://huggingface.co/ibm-granite/granite-4.1-8b-base) (Apache 2.0)
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- **Language:** English
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- **Finetuned from a base (not instruct) checkpoint** so output is the card itself, with no assistant-style preamble, disclaimers, or refusals.
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- **License:** Apache 2.0
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## Uses
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### Direct Use
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Generating SillyTavern-compatible character cards on demand from a natural-language request. The intended workflow is "describe a character, get a card," with the card output piped through a structural validator before import.
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### Out-of-Scope Use
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This is a single-turn card *generator*, not a roleplay or chat model — the assistant turn is a static card definition, not a conversation. It is not intended for multi-turn roleplay, as a general-purpose assistant, or for factual question answering.
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## How to Get Started
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The model was trained **without a system prompt**, so the cleanest usage is user-only. Use the chat template and sampling settings below.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer # transformers >= 5.0
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model_id = "aimeri/spoomplesmaxx-cardmaker-v1"
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tok = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")
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messages = [
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{"role": "user", "content": "Create a character card for a grumpy lighthouse keeper."},
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]
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inputs = tok.apply_chat_template(
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messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
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).to(model.device)
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out = model.generate(
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**inputs,
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max_new_tokens=8192,
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do_sample=True,
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temperature=1.0,
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top_k=64,
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top_p=0.95,
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repetition_penalty=1.1,
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)
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print(tok.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
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```
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Cards that include a `character_book` can be long; if generation cuts off mid-card, raise `max_new_tokens`. The merged 16-bit weights also serve directly under vLLM (`vllm serve aimeri/spoomplesmaxx-cardmaker-v1`), again with no system message.
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## Training Details
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### Procedure
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LoRA fine-tune with [Unsloth](https://github.com/unslothai/unsloth) + TRL `SFTTrainer`, using the official Granite 4.1 chat template. Loss was computed on the assistant (card) completion only via `train_on_responses_only`.
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**LoRA configuration**
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| Setting | Value |
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|---|---|
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| Rank `r` | 16 |
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| `lora_alpha` | 22 |
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| `lora_dropout` | 0 |
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| Target modules | all-linear |
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| Rank-stabilized LoRA | enabled |
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| Bias | none |
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**Training hyperparameters**
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| Setting | Value |
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|---|---|
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| Epochs | 2 (848 optimizer steps) |
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| Per-device batch size | 1 |
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| Gradient accumulation | 8 (effective batch size 8) |
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| Max sequence length | 8192 |
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| Optimizer | adamw_8bit (β₁ 0.9, β₂ 0.999, ε 1e-8) |
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| Learning rate | 1e-4, cosine schedule |
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| Warmup steps | 25 |
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| Weight decay | 0.001 |
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| Max grad norm | 1.0 |
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| Precision | bf16 |
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| Seed | 1985 |
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| Frameworks | Unsloth 2026.6.1, Transformers 5.5.0, TRL, PEFT, PyTorch 2.10 |
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### Results
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Evaluation loss on the 5% held-out split fell from the base checkpoint to the final model over the two epochs (most of the gain came in the first ~100 steps, with a slow grind afterward):
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| Checkpoint | Eval loss |
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|---|---|
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| Base (step 0, `eval_on_start`) | 2.234 |
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| Step 100 | 1.704 |
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| Step 400 | 1.656 |
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| Final (step 848) | **1.641** |
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Final mean training loss was ~1.57. Total wall-clock training time was ~4.6 hours.
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## Evaluation
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Quality was judged primarily **behaviorally** rather than by a single metric — eval loss is a weak proxy for card quality on a held-out set this small (~178 rows). A fixed prompt battery probed the behaviors that matter for this task:
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- **Structure & completeness** — clean, parseable cards with all expected fields on easy archetypes.
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- **Constraint adherence** — exact name / age / occupation, and a character's voice actually showing up in `first_mes` and `mes_example` rather than drifting generic.
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- **Sparse invention** — building a full, internally consistent card from a near-empty prompt.
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- **First-message craft** — second-person address to `{{user}}`, scene-setting, action formatting, in-voice dialogue, and a natural hand-off.
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- **Register** — antagonist/villain cards produced in-character, with no disclaimers, moralizing, or assistant-voice leakage. This is the main reason the model was trained from a base rather than an instruct checkpoint.
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## Bias, Risks, and Limitations
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- **Mature content.** This model was trained with a mix of Safe for Work and Not Safe For Work cards, and it may generate objectionable content. Please use discretion when generating new cards.
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- **Structural validity is not guaranteed.** Output is generated text, not schema-validated card JSON. Run it through a parser/validator before importing into SillyTavern.
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- **Card conventions.** Output uses `{{user}}` / `{{char}}` macros and assumes a SillyTavern runtime.
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- **Single-turn only.** This generates a card, not a conversation; it is not itself a roleplay partner.
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- **Inherited bias.** The model carries the biases of both the base model and the curated card sources, including their genre, aesthetic, and demographic skew. "High quality" reflects a subjective curation judgment.
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## Citation
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If you use this model, please reference this repository and the [base model](https://huggingface.co/ibm-granite/granite-4.1-8b-base).
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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-base",
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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": "2026.6.1",
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||||
"use_cache": false,
|
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"vocab_size": 100352
|
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}
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15
generation_config.json
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generation_config.json
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{
|
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"_from_model_config": true,
|
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"bos_token_id": 100257,
|
||||
"eos_token_id": [
|
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100257
|
||||
],
|
||||
"max_length": 131072,
|
||||
"pad_token_id": 100256,
|
||||
"do_sample": true,
|
||||
"repetition_penalty": 1.1,
|
||||
"temperature": 1.0,
|
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"top_k": 64,
|
||||
"top_p": 0.95,
|
||||
"transformers_version": "5.5.0"
|
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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
785
tokenizer_config.json
Normal file
785
tokenizer_config.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user