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Model: rare-engineer/MedAssistant-8B Source: Original Platform
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README.md
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---
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license: mit
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datasets:
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- dmis-lab/meerkat-instructions
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- HPAI-BSC/Medprompt-MedQA-CoT
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language:
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- en
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metrics:
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- rouge
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- accuracy
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base_model:
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- meta-llama/Llama-3.1-8B-Instruct
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pipeline_tag: text-generation
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---
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# MedAssistant-8B
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**MedAssistant-8B** is a LoRA fine-tuned LLM designed for advanced medical reasoning. The model is able to assist with medical diagnosis by providing detailed explanations in Chain of Thought (CoT).
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- 🧠 Base Model: <a href="https://github.com/marketplace/models/azureml-meta/Meta-Llama-3-1-8B-Instruct">Llama-3.1-8B-Instruct</a>
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- 🗂️ Dataset: <a href="https://huggingface.co/datasets/dmis-lab/meerkat-instructions/viewer/MedBooks-18-CoT">MedBooks-CoT-18</a> & <a href="https://huggingface.co/datasets/HPAI-BSC/Medprompt-MedQA-CoT">MedQA-CoT</a>
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- 🛠️ LoRA Parameters: r=128, α=64
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- ⚙️ Hardware: 4 x A40 GPUs
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## Model
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- Github repo: <a href="https://github.com/sabina-kairatova/MedAssist-8B">MedAssistant-8B</a>
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- Deploy: below provided a example code for direct inference with MedAssistant-8B.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained('rare-engineer/MedAssistant-8B',torch_dtype="auto",device_map="auto", use_safetensors= True)
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model.eval()
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tokenizer = AutoTokenizer.from_pretrained('rare-engineer/MedAssistant-8B', trust_remote_code=True, padding_side='left')
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user_instruction = "The following is a multiple-choice question about medical knowledge. Solve this in a step-by-step fashion, starting by summarizing the available information. Output a single option from the given options as the final answer."
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input_text = "A 17-year-old girl presents to the emergency department with a headache. The patient has had headaches in the past but this is the worst headache of her life. Her symptoms started yesterday and have been getting progressively worse. The patient states that the pain is mostly on the left side of her head. There has been a recent outbreak of measles at the patient’s school and the patient’s mother has been trying to give her daughter medicine to prevent her from getting sick. Her mother fears that her daughter may have caught measles. Her temperature is 98.6°F (37°C), blood pressure is 123/74 mmHg, pulse is 85/min, and respirations are 13/min. On exam, the patient is an obese girl who is clutching her head with the light in the room turned off. Her neurological exam is within normal limits. Fundoscopic exam reveals mild bilateral papilledema. An MRI of the head is obtained and reveals cerebral edema. A lumbar puncture reveals an increased opening pressure with a normal glucose level. Which of the following is the most likely diagnosis? A: Bacterial meningitis, B: Fat-soluble vitamin overuse, C: Migraine headache, D: Subarachnoid hemorrhage, E: Viral meningitis"
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inputs = tokenizer([user_instruction + '\n'+ input_text], return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=1024)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Training Piepline
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Supervised-Finetuning (SFT) using LoRA improves the LLM’s medical reasoning capability.
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- Llama-3.1-8B-Instruct was fine-tuned on 4-GPU:
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```bash
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torchrun --nproc_per_node=4 train.py \
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--model_name_or_path 'meta-llama/Llama-3.1-8B-Instruct' \
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--model_max_length 2048 \
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--data_dir /path/to/your/data \
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--output_dir /path/to/output/dir \
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--resume_from_checkpoint True \
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--gradient_checkpointing True \
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--ddp_find_unused_parameters False \
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--is_lora True \
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--lora_rank 128 \
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--lora_alpha 64 \
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--per_device_train_batch_size 4 \
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--gradient_accumulation_steps 2 \
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--optim "adamw_torch_fused" \
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--bf16 True \
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--tf32 True \
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--num_train_epochs 4 \
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--learning_rate 1.5e-4 \
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--lr_scheduler_type "cosine" \
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--warmup_ratio 0.05 \
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--weight_decay 0.01 \
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--logging_steps 1 \
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--save_steps 1000 \
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--eval_strategy "steps" \
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--eval_steps 250 \
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--save_total_limit 3 \
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--report_to "none"
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```
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## Evaluation
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- **Performance on Medical benchmarks**
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|Benchmarks| Base Model | Fully Fine-tuned Model | LoRA Fine-tuned Model|
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|----------|----------|----------|----------|
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| |<a href="https://github.com/marketplace/models/azureml-meta/Meta-Llama-3-1-8B-Instruct">Llama-3.1-8B-Instruct</a>| <a href="https://arxiv.org/abs/2504.00993">MedReason-8B</a>| MedAssist-8B |
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| **MedQA** | 58.7% | 71.8% | 69.3% |
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| **MedBullet-5** (test accuracy) | 40.9% | 55.5% | 54.2% |
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| **MedBullet-5** (Rouge-L) | 0.224 | N/A | 0.344 |
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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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{%- set date_string = "26 Jul 2024" %}
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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 + builtin tools #}
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{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
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{%- if builtin_tools is defined or tools is not none %}
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{{- "Environment: ipython\n" }}
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{%- endif %}
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{%- if builtin_tools is defined %}
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{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\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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{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
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{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
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{{- "<|python_tag|>" + tool_call.name + ".call(" }}
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{%- for arg_name, arg_val in tool_call.arguments | items %}
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{{- arg_name + '="' + arg_val + '"' }}
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{%- if not loop.last %}
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{{- ", " }}
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{%- endif %}
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{%- endfor %}
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{{- ")" }}
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{%- else %}
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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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{%- endif %}
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{%- if builtin_tools is defined %}
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{#- This means we're in ipython mode #}
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{{- "<|eom_id|>" }}
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{%- else %}
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{{- "<|eot_id|>" }}
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{%- endif %}
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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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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": "bfloat16",
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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": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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|
"intermediate_size": 14336,
|
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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": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
|
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"factor": 8.0,
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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_type": "llama3"
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|
},
|
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|
"rope_theta": 500000.0,
|
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"tie_word_embeddings": false,
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"transformers_version": "4.57.6",
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"use_cache": true,
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"vocab_size": 128256
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}
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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": "4.57.6"
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}
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model-00001-of-00004.safetensors
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model-00001-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:3aefefb2571af6c64c129ba22272f5a41e53dad8d059b4136b9461537898fcce
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size 4976698672
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model-00002-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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|
oid sha256:b89aea9916f055b5e94e899445326be3e0df7eb0100a3f78bf7a263e0d2c279d
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|
size 4999802720
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model-00003-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
|
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|
oid sha256:8e61854dda2938a2ca6c800c7d0b33e420fdff6b9b23603033c9b0599a4d2f04
|
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|
size 4915916176
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3
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3
model-00004-of-00004.safetensors
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|
||||||
|
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|
||||||
|
}
|
||||||
|
}
|
||||||
16
special_tokens_map.json
Normal file
16
special_tokens_map.json
Normal file
@@ -0,0 +1,16 @@
|
|||||||
|
{
|
||||||
|
"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
|
||||||
|
}
|
||||||
|
}
|
||||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2062
tokenizer_config.json
Normal file
2062
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user