Instructions to use Codemaster67/olmo_chem_250k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Codemaster67/olmo_chem_250k with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Codemaster67/olmo_chem_250k")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Codemaster67/olmo_chem_250k")
model = AutoModelForCausalLM.from_pretrained("Codemaster67/olmo_chem_250k", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Codemaster67/olmo_chem_250k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Codemaster67/olmo_chem_250k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Codemaster67/olmo_chem_250k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Codemaster67/olmo_chem_250k

SGLang

How to use Codemaster67/olmo_chem_250k with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Codemaster67/olmo_chem_250k" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Codemaster67/olmo_chem_250k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Codemaster67/olmo_chem_250k" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Codemaster67/olmo_chem_250k",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Codemaster67/olmo_chem_250k with Docker Model Runner:
```
docker model run hf.co/Codemaster67/olmo_chem_250k
```

OLMo-7B Full Fine-Tune — Chemistry SMILES CPT

Model Description

This model is a full-parameter fine-tuned version of Codemaster67/Olmo-7b-spe trained on chemistry SMILES strings from the Codemaster67/Causal_lm_chemistry_1M_rows dataset.

The base model's tokenizer was pre-extended with ~300 SPE (SMILES Pair Encoding) chemistry tokens plus <|start_of_smiles|> / <|end_of_smiles|> special tokens, and its embedding & LM-head layers were resized with mean-initialised vectors for the new tokens.

Training Details

Parameter	Value
Method	Full Fine-Tune (all weights updated)
Parallelism	FSDP (Fully Sharded Data Parallel)
Epochs	1
Learning Rate	5e-06
Batch Size (per device)	16
Gradient Accumulation	1
Max Sequence Length	512
Warmup Ratio	0.1
Weight Decay	0.01
Scheduler	Cosine
Precision	bf16
Augmentation	OFF
Training Samples	250000
Eval Samples	25000

Evaluation Results

Metric	Value
Final Eval Loss	0.9727568626403809
Final Eval Perplexity	2.645226943673604
Training Loss	1.1177

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("harindhar10/olmo_chem_lora_cpt_LoRA_500k", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("harindhar10/olmo_chem_lora_cpt_LoRA_500k", trust_remote_code=True)

smiles_input = "<|start_of_smiles|>CC(=O)Oc1ccccc1C(=O)O<|end_of_smiles|>"
inputs = tokenizer(smiles_input, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))

Intended Use

Chemistry-domain language modelling, SMILES generation and completion, and downstream molecular property prediction via fine-tuning.

Limitations

Trained primarily on SMILES strings; natural-language instruction-following ability may degrade compared to the base OLMo checkpoint.
Augmentation was disabled for this run.

Downloads last month: 50

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Codemaster67/olmo_chem_250k

Base model

Codemaster67/Olmo-7b-spe

Finetuned

(2)

this model

Duplicated from harindhar10/olmo_chem_lora_cpt_LoRA_500k

Codemaster67
/

olmo_chem_250k