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Model: teguhnandi/nandi Source: Original Platform
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98
README.md
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---
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library_name: transformers
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base_model: fine-tuned-llm-1B
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tags:
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- llm
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- lora
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- qlora
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- fine-tuning
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- health
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- chatbot
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- indonesian
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language:
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- id
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license: mit
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---
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# Chatbot Kesehatan Bahasa Indonesia (Fine-tuned LLM 1B)
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Ini adalah model bahasa besar (LLM) 1 miliar parameter yang telah di-fine-tune menggunakan teknik **QLoRA 4-bit Quantization** dengan dataset berbahasa Indonesia yang berfokus pada **informasi kesehatan**. Model ini dirancang untuk berfungsi sebagai chatbot asisten AI di bidang kesehatan secara umum.
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## Tujuan
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Model ini dioptimalkan untuk memberikan jawaban yang akurat dan helpful terkait pertanyaan-pertanyaan seputar kesehatan dalam Bahasa Indonesia. Fine-tuning ini bertujuan untuk membuat model lebih ahli dalam domain medis spesifik ini.
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## Dataset
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Dataset yang digunakan adalah kumpulan data berbahasa Indonesia terkait berbagai topik kesehatan, yang berisi berbagai intent, pola pertanyaan, dan respons relevan.
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## Parameter Fine-tuning
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- **Base Model**: Llama 3.2 1B Instruct (digunakan sebagai model dasar untuk fine-tuning)
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- **Teknik**: QLoRA (4-bit quantization)
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- **LoRA Config**:
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- `r=16`
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- `lora_alpha=32`
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- `lora_dropout=0.05`
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- `target_modules`: `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj`
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- **Training Arguments**:
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- `num_train_epochs=3`
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- `learning_rate=2e-4`
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- `per_device_train_batch_size=2`
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- `gradient_accumulation_steps=8`
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- `fp16=True`
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- `gradient_checkpointing=True`
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## Penggunaan (Inference)
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Anda dapat memuat model ini menggunakan library `transformers`:
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "teguhnandi/rijanandi"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.float16, # Pastikan menggunakan torch.float16 atau bfloat16
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device_map='auto',
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)
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def generate_response(prompt, model, tokenizer, max_length=256):
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system_prompt = """Anda adalah asisten AI yang ahli dalam kesehatan dan perawatan. Berikan jawaban yang akurat, helpful, dan berdasarkan pengetahuan medis yang terpercaya. Jika tidak yakin, sarankan untuk berkonsultasi dengan profesional kesehatan."""
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full_prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
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{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
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{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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"""
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inputs = tokenizer(full_prompt, return_tensors='pt').to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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**inputs,
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max_new_tokens=max_length,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id,
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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if "<|start_header_id|>assistant<|end_header_id|>" in response:
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response = response.split("<|start_header_id|>assistant<|end_header_id|>
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")[-1]
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response = response.replace("<|eot_id|>", "").strip()
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return response
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# Contoh penggunaan
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question = "Apa saja tips menjaga kesehatan secara umum?"
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answer = generate_response(question, model, tokenizer)
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print(f"Pertanyaan: {question}")
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print(f"Jawaban: {answer}")
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```
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## Catatan
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Model ini masih dalam tahap pengembangan dan dapat ditingkatkan lebih lanjut dengan dataset yang lebih luas dan fine-tuning lanjutan.
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93
chat_template.jinja
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chat_template.jinja
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{{- bos_token }}
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{%- if custom_tools is defined %}
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{%- set tools = custom_tools %}
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{%- endif %}
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{%- if not tools_in_user_message is defined %}
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{%- set tools_in_user_message = true %}
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{%- endif %}
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{%- if not date_string is defined %}
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{%- if strftime_now is defined %}
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{%- set date_string = strftime_now("%d %b %Y") %}
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{%- else %}
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{%- set date_string = "26 Jul 2024" %}
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{%- endif %}
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{%- endif %}
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{%- if not tools is defined %}
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{%- set tools = none %}
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{%- endif %}
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{#- This block extracts the system message, so we can slot it into the right place. #}
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{%- if messages[0]['role'] == 'system' %}
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{%- set system_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{%- set system_message = "" %}
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{%- endif %}
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{#- System message #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{{- "Cutting Knowledge Date: December 2023\n" }}
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{{- "Today Date: " + date_string + "\n\n" }}
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{%- if tools is not none and not tools_in_user_message %}
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{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{%- endif %}
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{{- system_message }}
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{{- "<|eot_id|>" }}
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{#- Custom tools are passed in a user message with some extra guidance #}
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{%- if tools_in_user_message and not tools is none %}
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{#- Extract the first user message so we can plug it in here #}
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{%- if messages | length != 0 %}
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{%- set first_user_message = messages[0]['content']|trim %}
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{%- set messages = messages[1:] %}
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{%- else %}
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{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
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{%- endif %}
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{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
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{{- "Given the following functions, please respond with a JSON for a function call " }}
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{{- "with its proper arguments that best answers the given prompt.\n\n" }}
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{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
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{{- "Do not use variables.\n\n" }}
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{%- for t in tools %}
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{{- t | tojson(indent=4) }}
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{{- "\n\n" }}
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{%- endfor %}
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{{- first_user_message + "<|eot_id|>"}}
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{%- endif %}
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{%- for message in messages %}
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{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
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{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
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{%- elif 'tool_calls' in message %}
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{%- if not message.tool_calls|length == 1 %}
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{{- raise_exception("This model only supports single tool-calls at once!") }}
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{%- endif %}
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{%- set tool_call = message.tool_calls[0].function %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- '{"name": "' + tool_call.name + '", ' }}
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{{- '"parameters": ' }}
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{{- tool_call.arguments | tojson }}
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{{- "}" }}
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{{- "<|eot_id|>" }}
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{%- elif message.role == "tool" or message.role == "ipython" %}
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{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
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{%- if message.content is mapping or message.content is iterable %}
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{{- message.content | tojson }}
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{%- else %}
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{{- message.content }}
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{%- endif %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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{%- endfor %}
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{%- if add_generation_prompt %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
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{%- endif %}
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40
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": 128000,
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"dtype": "float16",
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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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": 32,
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"num_hidden_layers": 16,
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"num_key_value_heads": 8,
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"pad_token_id": null,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_parameters": {
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_theta": 500000.0,
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"rope_type": "llama3"
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},
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"tie_word_embeddings": true,
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"transformers_version": "5.0.0",
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"use_cache": true,
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"vocab_size": 128256
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}
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "5.0.0"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:1bcebe4e712578856786ad252b538e5cceb68759de3ecca844f517b8e612a8e8
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size 2471645464
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BIN
tokenizer.json
(Stored with Git LFS)
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BIN
tokenizer.json
(Stored with Git LFS)
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tokenizer_config.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<|begin_of_text|>",
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"clean_up_tokenization_spaces": true,
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"eos_token": "<|eot_id|>",
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"is_local": false,
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"model_input_names": [
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"input_ids",
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"attention_mask"
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],
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"model_max_length": 131072,
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"pad_token": "<|eot_id|>",
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"tokenizer_class": "TokenizersBackend"
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}
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