初始化项目，由ModelHub XC社区提供模型
Model: Ephraimmm/customer-service Source: Original Platform
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111
README.md
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README.md
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---
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base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- gemma3
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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---
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# Nigerian Bank Customer Service Assistant
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## Overview
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This is a [Gemma-3-4B](https://huggingface.co/unsloth/gemma-3-4b-it-unsloth-bnb-4bit) instruction-tuned model fine-tuned to handle customer service conversations for a Nigerian bank. The fine-tuning data reflects Nigerian customer-service interactions, so the model's responses are tuned to Nigerian tone and phrasing patterns as they appear in text (e.g. common Nigerian English expressions and code-switching seen in chat/support transcripts). It was trained with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library using parameter-efficient (LoRA) fine-tuning.
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## Training Details
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| | |
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|---|---|
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| Base model | [unsloth/gemma-3-4b-it-unsloth-bnb-4bit](https://huggingface.co/unsloth/gemma-3-4b-it-unsloth-bnb-4bit) (Gemma 3, 4B, 4-bit quantized) |
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| Method | Supervised fine-tuning with LoRA (via [Unsloth](https://github.com/unslothai/unsloth) + Hugging Face TRL) |
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| LoRA rank (r) | 8 |
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| LoRA alpha | 8 |
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| LoRA dropout | 0 |
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| Target modules | Attention projections (`q_proj`, `k_proj`, `v_proj`, `o_proj`) and MLP projections (`gate_proj`, `up_proj`, `down_proj`) of the language model |
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| Task type | Causal LM |
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| Precision | float16 |
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This repository hosts the full fine-tuned model weights (merged, fp16, split across two safetensors shards) as well as the standalone LoRA adapter (`adapter_model.safetensors`) used to produce them, so the adapter can also be applied on top of the base model directly if preferred.
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Training data, dataset size, and number of training steps/epochs are not recorded in this repository's metadata and are not published here.
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## Intended Use
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- Financial services customer support conversations
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- Loan application assistance and status inquiries
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- Chat-based customer interaction with Nigerian tone and accent patterns in text
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This model is intended as a domain-specific assistant for Nigerian banking customer service scenarios rather than as a general-purpose chat model.
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## How to Use
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The repository contains the full merged model, so it can be loaded directly with `transformers` (no separate adapter step required):
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```python
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import torch
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from transformers import AutoProcessor, Gemma3ForConditionalGeneration
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model_id = "Ephraimmm/customer-service"
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model = Gemma3ForConditionalGeneration.from_pretrained(
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model_id,
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device_map="auto",
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torch_dtype=torch.float16,
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)
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processor = AutoProcessor.from_pretrained(model_id)
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messages = [
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{"role": "user", "content": [{"type": "text", "text": "Good day, please I want to check on my loan application status."}]}
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]
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inputs = processor.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=True,
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return_dict=True,
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return_tensors="pt",
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).to(model.device)
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with torch.no_grad():
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output_ids = model.generate(**inputs, max_new_tokens=256)
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response = processor.decode(
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output_ids[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True
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)
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print(response)
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```
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Alternatively, load with Unsloth for faster inference:
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```python
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from unsloth import FastModel
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model, tokenizer = FastModel.from_pretrained(
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model_name="Ephraimmm/customer-service",
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max_seq_length=2048,
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load_in_4bit=True,
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)
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FastModel.for_inference(model)
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```
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If you only want the LoRA weights, `adapter_config.json` / `adapter_model.safetensors` in this repo can be loaded on top of the base model `unsloth/gemma-3-4b-it-unsloth-bnb-4bit` with `peft`.
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## Limitations
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- This is a domain-specific model tuned for a narrow use case (Nigerian bank customer service, loan-related queries); it has not been evaluated against standard NLP/LLM benchmarks.
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- No formal evaluation metrics, accuracy figures, or benchmark scores are published for this model — do not assume performance figures not stated here.
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- As with any fine-tuned LLM, outputs should be reviewed before use in a production financial-services context, particularly for compliance-sensitive communication.
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- The base architecture (Gemma 3) supports multimodal (image+text) input, but this fine-tune was produced for text-based customer service interaction.
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## Author
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Developed by [Ephraimmm](https://huggingface.co/Ephraimmm)
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This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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38
adapter_config.json
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"parent_library": "transformers.models.gemma3.modeling_gemma3",
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"unsloth_fixed": true
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},
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47
chat_template.jinja
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chat_template.jinja
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{{ bos_token }}
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{%- if messages[0]['role'] == 'system' -%}
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{%- if messages[0]['content'] is string -%}
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{%- set first_user_prefix = messages[0]['content'] + '
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' -%}
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{%- else -%}
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{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
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' -%}
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{%- endif -%}
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{%- set loop_messages = messages[1:] -%}
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{%- else -%}
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{%- set first_user_prefix = "" -%}
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{%- set loop_messages = messages -%}
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{%- endif -%}
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{%- for message in loop_messages -%}
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{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
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{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
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{%- endif -%}
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{%- if (message['role'] == 'assistant') -%}
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{%- set role = "model" -%}
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{%- else -%}
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{%- set role = message['role'] -%}
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{%- endif -%}
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{{ '<start_of_turn>' + role + '
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' + (first_user_prefix if loop.first else "") }}
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{%- if message['content'] is string -%}
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{%- if item['type'] == 'image' -%}
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{{ '<start_of_image>' }}
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{%- elif item['type'] == 'text' -%}
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{{ item['text'] | trim }}
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{%- endfor -%}
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{%- else -%}
|
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{{ raise_exception("Invalid content type") }}
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{%- endif -%}
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{{ '<end_of_turn>
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' }}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{ '<start_of_turn>model
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' }}
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{%- endif -%}
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config.json
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"vision_tower.vision_model.encoder.layers.9.self_attn.k_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_tower.vision_model.encoder.layers.9.self_attn.out_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_tower.vision_model.encoder.layers.9.self_attn.out_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_tower.vision_model.encoder.layers.9.self_attn.q_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_tower.vision_model.encoder.layers.9.self_attn.q_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_tower.vision_model.encoder.layers.9.self_attn.v_proj.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_tower.vision_model.encoder.layers.9.self_attn.v_proj.weight": "model-00001-of-00002.safetensors",
|
||||
"vision_tower.vision_model.post_layernorm.bias": "model-00001-of-00002.safetensors",
|
||||
"vision_tower.vision_model.post_layernorm.weight": "model-00001-of-00002.safetensors"
|
||||
}
|
||||
}
|
||||
29
preprocessor_config.json
Normal file
29
preprocessor_config.json
Normal file
@@ -0,0 +1,29 @@
|
||||
{
|
||||
"do_convert_rgb": null,
|
||||
"do_normalize": true,
|
||||
"do_pan_and_scan": null,
|
||||
"do_rescale": true,
|
||||
"do_resize": true,
|
||||
"image_mean": [
|
||||
0.5,
|
||||
0.5,
|
||||
0.5
|
||||
],
|
||||
"image_processor_type": "Gemma3ImageProcessor",
|
||||
"image_seq_length": 256,
|
||||
"image_std": [
|
||||
0.5,
|
||||
0.5,
|
||||
0.5
|
||||
],
|
||||
"pan_and_scan_max_num_crops": null,
|
||||
"pan_and_scan_min_crop_size": null,
|
||||
"pan_and_scan_min_ratio_to_activate": null,
|
||||
"processor_class": "Gemma3Processor",
|
||||
"resample": 2,
|
||||
"rescale_factor": 0.00392156862745098,
|
||||
"size": {
|
||||
"height": 896,
|
||||
"width": 896
|
||||
}
|
||||
}
|
||||
4
processor_config.json
Normal file
4
processor_config.json
Normal file
@@ -0,0 +1,4 @@
|
||||
{
|
||||
"image_seq_length": 256,
|
||||
"processor_class": "Gemma3Processor"
|
||||
}
|
||||
33
special_tokens_map.json
Normal file
33
special_tokens_map.json
Normal file
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"boi_token": "<start_of_image>",
|
||||
"bos_token": {
|
||||
"content": "<bos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eoi_token": "<end_of_image>",
|
||||
"eos_token": {
|
||||
"content": "<end_of_turn>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"image_token": "<image_soft_token>",
|
||||
"pad_token": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:4667f2089529e8e7657cfb6d1c19910ae71ff5f28aa7ab2ff2763330affad795
|
||||
size 33384568
|
||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
|
||||
size 4689074
|
||||
51347
tokenizer_config.json
Normal file
51347
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user