---
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
pipeline_tag: text-generation
tags:
- chemistry
- retrosynthesis
- reaction-prediction
- qwen2.5
- merged-lora
---

# retro-agent-qwen25-7b-merged-fp16

Standalone fp16 retrosynthesis assistant model for serving. The model is based on `Qwen/Qwen2.5-7B-Instruct` with these LoRA adapters merged into the base weights:

- retro-reactants-qwen25-7b
- retro-conditions-qwen25-7b

Because the LoRA weights are merged, deployment does not need PEFT adapter loading. Load and serve this repository as a normal causal language model.

## Intended Use

This model is intended for structured retrosynthesis assistance:

- predict likely reactants for a target product SMILES
- predict plausible reaction conditions
- return machine-readable JSON for downstream validation and rendering

Use RDKit or another chemistry toolkit to validate generated SMILES before acting on model output.

## Prompt Templates

Use explicit task labels and request JSON only. Low temperature is recommended for repeatable API calls.

### Reactant Prediction

```text
System: You are a retrosynthesis assistant. Return valid JSON only.
User: Task: predict_reactants
Product SMILES: CC(=O)Oc1ccccc1C(=O)O
Return JSON with keys: reactants_smiles, confidence, rationale.
```

### Condition Prediction

```text
System: You are a reaction condition assistant. Return valid JSON only.
User: Task: predict_conditions
Product SMILES: CC(=O)Oc1ccccc1C(=O)O
Reactants SMILES: CC(=O)O.Oc1ccccc1C(=O)O
Return JSON with keys: solvent, reagents, catalyst, temperature_celsius, time, confidence, rationale.
```

### Combined Planning Prompt

```text
System: You are a retrosynthesis assistant. Return valid JSON only.
User: Task: retrosynthesis_step
Target product SMILES: CC(=O)Oc1ccccc1C(=O)O
Return one JSON object with keys: reactants_smiles, conditions, confidence, rationale.
```

## Python Example

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "<your-namespace>/retro-agent-qwen25-7b-merged-fp16"

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,
)

messages = [
    {"role": "system", "content": "You are a retrosynthesis assistant. Return valid JSON only."},
    {"role": "user", "content": "Task: retrosynthesis_step\nTarget product SMILES: CC(=O)Oc1ccccc1C(=O)O\nReturn one JSON object with keys: reactants_smiles, conditions, confidence, rationale."},
]

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

with torch.no_grad():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.1,
        do_sample=False,
    )

new_tokens = output_ids[0][inputs.input_ids.shape[-1]:]
print(tokenizer.decode(new_tokens, skip_special_tokens=True))
```

## REST API Examples

### Hugging Face Router / OpenAI-compatible Chat Completions

This works only if the model is available through Hugging Face Inference Providers. For custom merged models, a dedicated endpoint is often more reliable.

```bash
export HF_TOKEN=hf_...

curl https://router.huggingface.co/v1/chat/completions \
  -H "Authorization: Bearer $HF_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "<your-namespace>/retro-agent-qwen25-7b-merged-fp16",
    "messages": [
      {"role": "system", "content": "You are a retrosynthesis assistant. Return valid JSON only."},
      {"role": "user", "content": "Task: retrosynthesis_step\nTarget product SMILES: CC(=O)Oc1ccccc1C(=O)O"}
    ],
    "temperature": 0.1,
    "max_tokens": 512,
    "stream": false
  }'
```

### Dedicated Hugging Face Inference Endpoint

Create a dedicated endpoint from this model repository, then call the endpoint URL directly.

```bash
export HF_TOKEN=hf_...
export ENDPOINT_URL=https://<your-endpoint>.<region>.<provider>.endpoints.huggingface.cloud

curl "$ENDPOINT_URL" \
  -X POST \
  -H "Authorization: Bearer $HF_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "inputs": "Task: retrosynthesis_step\nTarget product SMILES: CC(=O)Oc1ccccc1C(=O)O\nReturn one JSON object with keys: reactants_smiles, conditions, confidence, rationale.",
    "parameters": {
      "max_new_tokens": 512,
      "temperature": 0.1,
      "return_full_text": false
    }
  }'
```

## Notes

This model merges adapters trained for related but distinct tasks. Evaluate both reactant prediction and condition prediction after merge, because independently trained LoRA adapters can interfere with each other.