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Model: yashm/qwen25-15b-biomed-finetuned
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---
library_name: transformers
tags:
- qwen
- biomedical
- bioinformatics
- fine-tuned
- medical
- llm
license: apache-2.0
base_model:
- Qwen/Qwen2.5-1.5B
---
# Qwen2.5-1.5B Biomedical Fine-Tuned Model
This model is a biomedical and bioinformatics fine-tuned version of **Qwen/Qwen2.5-1.5B**, fine-tuned by **Dr. YMG**.
---
## Model Details
### Model Description
This model is a domain-adapted and instruction fine-tuned large language model specialized for biomedical and bioinformatics tasks.
- Developed by: Dr. YMG
- Model type: Causal Language Model (LLM)
- Language(s): English
- License: Apache 2.0
- Finetuned from model: Qwen/Qwen2.5-1.5B
### Model Sources
- Repository: https://huggingface.co/yashm/qwen25-15b-biomed-finetuned
- Base Model: https://huggingface.co/Qwen/Qwen2.5-1.5B
---
## Uses
### Direct Use
- Biomedical concept explanation
- Bioinformatics discussions
- Research assistance
- Literature summarization
- Gene expression & biomarker discussion
### Out-of-Scope Use
- Clinical diagnosis
- Medical treatment decisions
- Drug prescription
- Patient-specific advice
---
## Example Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
MODEL_ID = "yashm/qwen25-15b-biomed-finetuned"
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
MODEL_ID,
device_map="auto",
dtype=torch.bfloat16,
trust_remote_code=True,
)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
prompt = "Explain gene expression in simple terms."
out = pipe(prompt, max_new_tokens=200)
print(out[0]["generated_text"])
```
---
## Training Details
- Base model: Qwen/Qwen2.5-1.5B
- Method: LoRA (PEFT)
- Precision: BF16
- Quantization: 4-bit QLoRA
---
## Limitations
- May hallucinate
- Not medically validated
- Limited to training data
---
## Disclaimer
For research and educational use only. Not for clinical decision-making.
---
## Author
Fine-tuned by Dr. YMG

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function 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><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
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{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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{
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