DeepSeek-V2-medical

This repository contains a 4-bit LoRA adapter fine-tuned on top of deepseek-ai/DeepSeek-V2-Lite for medical treatment planning.

• Base model: deepseek-ai/DeepSeek-V2-Lite (4-bit quantized)
• Adapter: LoRA, trained on clinical vignette → treatment plan pairs
• Tokenizer: same as base, with pad_token set to eos

Usage

from transformers import AutoTokenizer, BitsAndBytesConfig
from peft         import PeftModel
import torch

# 1) Load tokenizer + adapter
tokenizer = AutoTokenizer.from_pretrained(
    "CodCodingCode/DeepSeek-V2-medical",
    trust_remote_code=True
)
tokenizer.pad_token_id = tokenizer.pad_token_id or tokenizer.eos_token_id

bnb = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)

# 2) Reload the base quantized model
from transformers import AutoModelForCausalLM
base = AutoModelForCausalLM.from_pretrained(
    "deepseek-ai/DeepSeek-V2-Lite",
    quantization_config=bnb,
    device_map="auto",
    trust_remote_code=True,
)
base.resize_token_embeddings(len(tokenizer))

# 3) Attach your LoRA adapter
model = PeftModel.from_pretrained(
    base,
    "CodCodingCode/DeepSeek-V2-medical",
    device_map="auto",
    torch_dtype=torch.float16,
    trust_remote_code=True,
)
model.config.use_cache = False  # match your training config

# 4) Generate
prompt = (
    "### Instruction:\n"
    "You are a board-certified clinician ...\n\n"
    "### Input:\n"
    "THINKING: ...\n\n"
    "### Response:\n"
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.2,
    top_p=0.95,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

This repository contains a 4-bit LoRA fine-tuned adapter on top of [deepseek-ai/DeepSeek-V2-Lite](https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite) for medical treatment planning.

## Model Card

- **Base model:** `deepseek-ai/DeepSeek-V2-Lite` (4-bit quantized)  
- **Adapter:** LoRA, trained on clinical vignette→treatment pairs  
- **Tokenizer:** same as base, with pad_token set to eos  

## Usage

```python
from transformers import AutoTokenizer, BitsAndBytesConfig
from peft        import PeftModel
import torch

# 1) Load tokenizer + adapter
tokenizer = AutoTokenizer.from_pretrained(
    "CodCodingCode/DeepSeek-V2-medical", trust_remote_code=True
)
tokenizer.pad_token_id = tokenizer.pad_token_id or tokenizer.eos_token_id

# 2) Reload quantized base
bnb = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.float16,
)
base = AutoModelForCausalLM.from_pretrained(
    "deepseek-ai/DeepSeek-V2-Lite",
    quantization_config=bnb,
    device_map="auto",
    trust_remote_code=True,
)
base.resize_token_embeddings(len(tokenizer))

# 3) Attach LoRA adapter
model = PeftModel.from_pretrained(
    base,
    "CodCodingCode/DeepSeek-V2-medical",
    device_map="auto",
    trust_remote_code=True,
)
model.config.use_cache = False

# 4) Generate text
prompt = (
    "### Instruction:\n"
    "You are a board-certified clinician. Based on the following patient vignette, "
    "suggest a concise treatment plan:\n\n"
    "### Input:\n"
    "A 65-year-old presents with chronic shortness of breath and persistent cough...\n\n"
    "### Response:\n"
)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs,
    max_new_tokens=128,
    do_sample=True,
    top_p=0.9,
    temperature=0.8,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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