Rumiii

Qwen2.5-0.5B-Med-Post-Trained-92k

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Training Pipeline

Stage 1 — Continued Pre-Training (CPT)

Table with columns: Property, Value
Property	Value
Base model	Qwen/Qwen2.5-0.5B
Training type	Full-parameter CPT (no LoRA)
Dataset	VietAI/vi_pubmed (92k English abstracts)
Tokens	~23.6 million
Objective	Causal Language Modeling (CLM)
Optimizer	AdamW 8-bit
Learning rate	2e-5 cosine
Hardware	Kaggle Tesla T4
Training time	~3h 45m
Loss	2.581 → 2.478

Stage 2 — Supervised Fine-Tuning (SFT)

Table with columns: Property, Value
Property	Value
Base model	Rumiii/Qwen2.5-0.5B-Med-Pre-Trained-92k
Training type	Full-parameter SFT (no LoRA)
Dataset	causal-lm/ultrachat (20k samples)
Format	Qwen2.5 ChatML chat template
Optimizer	AdamW 8-bit
Learning rate	2e-5 cosine
Hardware	Kaggle Tesla T4
Training time	~1h 13m
Loss

Usage

This model uses the Qwen2.5 ChatML chat template. A system prompt is required for best results. The recommended inference setup is shown below.

Recommended system prompt

python
SYSTEM_PROMPT = (
    "You are a knowledgeable medical AI assistant named MedAssist. "
    "Answer all questions clearly, directly, and informatively. "
    "For medical questions provide accurate information. "
    "Never generate multiple choice questions unless explicitly asked."
)

Basic inference

python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Rumiii/Qwen2.5-0.5B-Med-Post-Trained-92k"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)
model.eval()

SYSTEM_PROMPT = (
    "You are a knowledgeable medical AI assistant named MedAssist. "
    "Answer all questions clearly, directly, and informatively. "
    "For medical questions provide accurate information. "
    "Never generate multiple choice questions unless explicitly asked."
)

messages = [
    {"role": "system",  "content": SYSTEM_PROMPT},
    {"role": "user",    "content": "What are the symptoms of pneumonia?"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

im_end_id = tokenizer.convert_tokens_to_ids("<|im_end|>")
stop_ids = [tokenizer.eos_token_id]
if im_end_id and im_end_id != tokenizer.eos_token_id:
    stop_ids.append(im_end_id)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.2,
        top_p=0.9,
        repetition_penalty=1.15,
        do_sample=True,
        eos_token_id=stop_ids,
        pad_token_id=tokenizer.eos_token_id,
    )

new_tokens = outputs[0][inputs["input_ids"].shape[-1]:]
response = tokenizer.decode(new_tokens, skip_special_tokens=True).strip()
print(response)

Recommended generation parameters

Table with columns: Parameter, Value, Reason
Parameter	Value	Reason
temperature	0.2	Low temperature for factual medical responses
top_p	0.9	Stable token sampling
repetition_penalty	1.15	Prevents response loops
max_new_tokens	512	Sufficient for complete answers
eos_token_id	include `<\|im_end\|>`	Required to stop at turn boundary

Important note on greetings

As a 0.5B model fine-tuned on instruction data, this model may produce inconsistent responses to simple greetings such as "Hi" or "Hello." It performs best when given direct questions or requests. For production deployments, greeting inputs should be handled with a fixed response rather than passed to the model.

Intended Use

Medical question answering and clinical education
Research into small biomedical language models
Lightweight medical AI prototyping
Demonstration of CPT + SFT pipeline on consumer hardware

Not Intended For

Clinical decision making in real patient care
Diagnostic or treatment decisions
Replacement of licensed medical professionals

Limitations

494M parameters — reasoning depth is limited compared to larger models
Trained on single-turn instruction pairs — multi-turn coherence is basic
Clinical accuracy not guaranteed — all outputs require expert verification
Simple greetings may produce inconsistent responses at this model scale
English only

Author

Rumi Iqbal Sufi Graduate Trainee, Excelra Knowledge Solutions, Hyderabad HuggingFace: Rumiii GitHub: sufirumii arXiv: 2506.09513

Model provider

Rumiii

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Base

Qwen/Qwen2.5-0.5B

Fine-tuned

this model

Modalities

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Output

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Training Pipeline

Stage 1 — Continued Pre-Training (CPT)

Table with columns: Property, Value
Property	Value
Base model	Qwen/Qwen2.5-0.5B
Training type	Full-parameter CPT (no LoRA)
Dataset	VietAI/vi_pubmed (92k English abstracts)
Tokens	~23.6 million
Objective	Causal Language Modeling (CLM)
Optimizer	AdamW 8-bit
Learning rate	2e-5 cosine
Hardware	Kaggle Tesla T4
Training time	~3h 45m
Loss	2.581 → 2.478

Stage 2 — Supervised Fine-Tuning (SFT)

Table with columns: Property, Value
Property	Value
Base model	Rumiii/Qwen2.5-0.5B-Med-Pre-Trained-92k
Training type	Full-parameter SFT (no LoRA)
Dataset	causal-lm/ultrachat (20k samples)
Format	Qwen2.5 ChatML chat template
Optimizer	AdamW 8-bit
Learning rate	2e-5 cosine
Hardware	Kaggle Tesla T4
Training time	~1h 13m
Loss

Usage

This model uses the Qwen2.5 ChatML chat template. A system prompt is required for best results. The recommended inference setup is shown below.

Recommended system prompt

python
SYSTEM_PROMPT = (
    "You are a knowledgeable medical AI assistant named MedAssist. "
    "Answer all questions clearly, directly, and informatively. "
    "For medical questions provide accurate information. "
    "Never generate multiple choice questions unless explicitly asked."
)

Basic inference

python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "Rumiii/Qwen2.5-0.5B-Med-Post-Trained-92k"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)
model.eval()

SYSTEM_PROMPT = (
    "You are a knowledgeable medical AI assistant named MedAssist. "
    "Answer all questions clearly, directly, and informatively. "
    "For medical questions provide accurate information. "
    "Never generate multiple choice questions unless explicitly asked."
)

messages = [
    {"role": "system",  "content": SYSTEM_PROMPT},
    {"role": "user",    "content": "What are the symptoms of pneumonia?"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

im_end_id = tokenizer.convert_tokens_to_ids("<|im_end|>")
stop_ids = [tokenizer.eos_token_id]
if im_end_id and im_end_id != tokenizer.eos_token_id:
    stop_ids.append(im_end_id)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.2,
        top_p=0.9,
        repetition_penalty=1.15,
        do_sample=True,
        eos_token_id=stop_ids,
        pad_token_id=tokenizer.eos_token_id,
    )

new_tokens = outputs[0][inputs["input_ids"].shape[-1]:]
response = tokenizer.decode(new_tokens, skip_special_tokens=True).strip()
print(response)

Recommended generation parameters

Table with columns: Parameter, Value, Reason
Parameter	Value	Reason
temperature	0.2	Low temperature for factual medical responses
top_p	0.9	Stable token sampling
repetition_penalty	1.15	Prevents response loops
max_new_tokens	512	Sufficient for complete answers
eos_token_id	include `<\|im_end\|>`	Required to stop at turn boundary

Important note on greetings

Intended Use

Medical question answering and clinical education
Research into small biomedical language models
Lightweight medical AI prototyping
Demonstration of CPT + SFT pipeline on consumer hardware

Not Intended For

Clinical decision making in real patient care
Diagnostic or treatment decisions
Replacement of licensed medical professionals

Limitations

494M parameters — reasoning depth is limited compared to larger models
Trained on single-turn instruction pairs — multi-turn coherence is basic
Clinical accuracy not guaranteed — all outputs require expert verification
Simple greetings may produce inconsistent responses at this model scale
English only

Author

Rumi Iqbal Sufi Graduate Trainee, Excelra Knowledge Solutions, Hyderabad HuggingFace: Rumiii GitHub: sufirumii arXiv: 2506.09513

Qwen2.5-0.5B-Med-Post-Trained-92k

Get help setting up a custom Dedicated Endpoints.

README

Training Pipeline

Stage 1 — Continued Pre-Training (CPT)

Stage 2 — Supervised Fine-Tuning (SFT)

Usage

Recommended system prompt

Basic inference

Recommended generation parameters

Important note on greetings

Intended Use

Not Intended For

Limitations

Author

Explore FriendliAI today

README

Training Pipeline

Stage 1 — Continued Pre-Training (CPT)

Stage 2 — Supervised Fine-Tuning (SFT)

Usage

Recommended system prompt

Basic inference

Recommended generation parameters

Important note on greetings

Intended Use

Not Intended For

Limitations

Author