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Model: xaviergillard/digita
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
+library_name: peft
+pipeline_tag: text-generation
+tags:
+- base_model:adapter:unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
+- lora
+- transformers
+- unsloth
+---
+
+# Model Card for Digita
+
+Digita has been fine-tuned (qlora) on a proprietary dataset so as to respond 
+similarly to the Belgian State Archive customer support. It was essentially
+trained using data from the "digit" mailbox from the DiVa section.
+
+
+## Model Details
+
+### Model Description
+
+Digita has been trained in the context of the 
+[Arkey project](https://www.uclouvain.be/fr/instituts-recherche/ilc/miil/arkey)
+conjointly held by the Belgian State Archive and UCLouvain. The purpose of this
+project is to ease access to the archives held at both institutions for the
+general public.
+
+In that context, Digita has been trained as an experiment to tune a chatbot
+that would be able to talk and reply to user request in a way that feels 
+similar to the actual people dealing with the matter at the BSA.
+
+
+- **Developed by:** Xavier GILLARD
+- **Funded by BELSPO:** [Arkey project](https://www.uclouvain.be/fr/instituts-recherche/ilc/miil/arkey)
+- **Model type:** Conversational (Fine Tuned from Llama-3.1-8b-Instruct-bnb-4bit)
+- **Language(s) (NLP):** Dutch, French, German, English
+- **License:** MIT License
+- **Finetuned from model Llama-3.1-8b-Instruct:** [unsloth/meta-llama-3.1-8b-instruct-bnb-4bit](https://huggingface.co/unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit)
+
+### Model Sources
+
+All scripts, notebooks, etc.. (except the dataset data) that have been used
+to prepare the dataset and fine tune the model are [available on github](https://github.com/xgillard/corpus_digit)
+
+
+**Repository:** [xgillard/corpus_digit](https://github.com/xgillard/corpus_digit)
+
+## Uses
+
+Prototype a chatbot to help the users of the Belgian State Archive in the 
+context of the Arkey project.
+
+## Limitations
+
+This model has been fine tuned on data from the 2022-2024 period. Which means
+the majority of the data predates the introduction of the AGATHA platform.
+This means that, while some of the info provided by this model can be useful,
+it is essentially going to be stale at this point.
+
+The second major limitation I envision for this model stems from the sheer size
+of the model: 8b is probably not going to cut it for a majority of cases. 
+Especially if one envisions to use the quantized versions.
+However, I think that this model is a good proof of concept and that it shows
+what could be achieved if resources were allocated to perform the same task 
+on a more up-to-date dataset (or limited to 2024) using a larger model.
+This was however not feasible on my personal machine.
+
+### Recommendations
+
+If anyone is serious about using this model as a basis to help the BSA personnel
+of to deploy an user facing chatbot at the BSA, I would recommend to completely
+redo all the finetuning using a larger model and an updated (more recent)
+version of the dataset.
+
+## How to Get Started with the Model
+
+I do recommend that you use the QLoRA adapter directly using unsloth as this is
+the asset that seemed to yield the best result while maintaining a similar 
+VRAM footprint (about 7G). Here is how you get started:
+
+```
+from unsloth import (
+    FastLanguageModel, 
+    train_on_responses_only,
+)
+from unsloth.chat_templates import (
+    get_chat_template,
+)
+from transformers import TextStreamer
+
+model, tok = FastLanguageModel.from_pretrained("xaviergillard/digita", load_in_4bit=True)
+model = FastLanguageModel.for_inference(model)
+```
+
+## Training Details
+
+### Training Data
+
+Original training data consists of a curated dataset of response emails sent 
+by the DiVa cusomer service. The details of this dataset will not be disclosed.
+
+### Training Procedure
+
+#### Preprocessing
+
+1. Cleaning up the encoding
+2. Conversation restructuration
+3. Conversation classification
+4. Data Augmentation
+5. Filtering
+
+#### Training Hyperparameters
+
+- **Training regime:** QLoRA adapter based on a 4bit quantization of Llama-3.1-8b-Instruct by unsloth. 
+- **LoRA rank:** `rank = 16`
+- **LoRA alpha:** `alpha = 16`
+- **Base model quantization:** `4 bits`
+
+All training/eval metrics can be consulted [here](https://www.comet.com/xaviergillard/digita/view/new/panels).
+
+
+## Evaluation
+
+So far, evaluation has not been very thorough: it mostly consisted of
+* comparing the eval loss to the training loss during training
+* ensuring the curves seemed ok
+* peforming a few manual tests on the trained & quantized models.
+
+## Model Card Authors
+
+Xavier Gillard
+
+### Framework versions
+
+- PEFT 0.18.0

adapter_config.json

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+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "LlamaForCausalLM",
+    "parent_library": "transformers.models.llama.modeling_llama",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "unsloth/meta-llama-3.1-8b-instruct-bnb-4bit",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.0",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "gate_proj",
+    "down_proj",
+    "q_proj",
+    "o_proj",
+    "k_proj",
+    "v_proj",
+    "up_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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chat_template.jinja

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+{{- bos_token }}
+{%- if custom_tools is defined %}
+    {%- set tools = custom_tools %}
+{%- endif %}
+{%- if not tools_in_user_message is defined %}
+    {%- set tools_in_user_message = true %}
+{%- endif %}
+{%- if not date_string is defined %}
+    {%- set date_string = "26 July 2024" %}
+{%- endif %}
+{%- if not tools is defined %}
+    {%- set tools = none %}
+{%- endif %}
+
+{#- This block extracts the system message, so we can slot it into the right place. #}
+{%- if messages[0]['role'] == 'system' %}
+    {%- set system_message = messages[0]['content'] %}
+    {%- set messages = messages[1:] %}
+{%- else %}
+    {%- set system_message = "" %}
+{%- endif %}
+
+{#- System message + builtin tools #}
+{{- "<|start_header_id|>system<|end_header_id|>
+
+" }}
+{%- if builtin_tools is defined or tools is not none %}
+    {{- "Environment: ipython
+" }}
+{%- endif %}
+{%- if builtin_tools is defined %}
+    {{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "
+
+"}}
+{%- endif %}
+{{- "Cutting Knowledge Date: December 2023
+" }}
+{{- "Today Date: " + date_string + "
+
+" }}
+{%- if tools is not none and not tools_in_user_message %}
+    {{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.
+
+" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "
+
+" }}
+    {%- endfor %}
+{%- endif %}
+{{- system_message }}
+{{- "<|eot_id|>" }}
+
+{#- Custom tools are passed in a user message with some extra guidance #}
+{%- if tools_in_user_message and not tools is none %}
+    {#- Extract the first user message so we can plug it in here #}
+    {%- if messages | length != 0 %}
+        {%- set first_user_message = messages[0]['content'] %}
+        {%- set messages = messages[1:] %}
+    {%- else %}
+        {{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
+{%- endif %}
+    {{- '<|start_header_id|>user<|end_header_id|>
+
+' -}}
+    {{- "Given the following functions, please respond with a JSON for a function call " }}
+    {{- "with its proper arguments that best answers the given prompt.
+
+" }}
+    {{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
+    {{- "Do not use variables.
+
+" }}
+    {%- for t in tools %}
+        {{- t | tojson(indent=4) }}
+        {{- "
+
+" }}
+    {%- endfor %}
+    {{- first_user_message + "<|eot_id|>"}}
+{%- endif %}
+
+{%- for message in messages %}
+    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
+        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>
+
+'+ message['content'] + '<|eot_id|>' }}
+    {%- elif 'tool_calls' in message %}
+        {%- if not message.tool_calls|length == 1 %}
+            {{- raise_exception("This model only supports single tool-calls at once!") }}
+        {%- endif %}
+        {%- set tool_call = message.tool_calls[0].function %}
+        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>
+
+' -}}
+            {{- "<|python_tag|>" + tool_call.name + ".call(" }}
+            {%- for arg_name, arg_val in tool_call.arguments | items %}
+                {{- arg_name + '="' + arg_val + '"' }}
+                {%- if not loop.last %}
+                    {{- ", " }}
+                {%- endif %}
+                {%- endfor %}
+            {{- ")" }}
+        {%- else  %}
+            {{- '<|start_header_id|>assistant<|end_header_id|>
+
+' -}}
+            {{- '{"name": "' + tool_call.name + '", ' }}
+            {{- '"parameters": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- "}" }}
+        {%- endif %}
+        {%- if builtin_tools is defined %}
+            {#- This means we're in ipython mode #}
+            {{- "<|eom_id|>" }}
+        {%- else %}
+            {{- "<|eot_id|>" }}
+        {%- endif %}
+    {%- elif message.role == "tool" or message.role == "ipython" %}
+        {{- "<|start_header_id|>ipython<|end_header_id|>
+
+" }}
+        {%- if message.content is mapping or message.content is iterable %}
+            {{- message.content | tojson }}
+        {%- else %}
+            {{- message.content }}
+        {%- endif %}
+        {{- "<|eot_id|>" }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|start_header_id|>assistant<|end_header_id|>
+
+' }}
+{%- endif %}

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+        "LlamaForCausalLM"
+    ],
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+    "transformers_version": "4.57.3",
+    "unsloth_fixed": true,
+    "unsloth_version": "2025.12.9",
+    "use_cache": true,
+    "vocab_size": 128256
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+}

special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|finetune_right_pad_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}