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_chem_250k / README.md

Codemaster67

Duplicate from harindhar10/olmo_chem_lora_cpt_LoRA_500k

0b50eaf 2 days ago

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	---
	base_model: Codemaster67/Olmo-7b-spe
	datasets:
	- Codemaster67/Causal_lm_chemistry_1M_rows
	language: en
	library_name: transformers
	license: apache-2.0
	tags:
	- chemistry
	- smiles
	- olmo
	- causal-lm
	- full-finetune
	- fsdp
	---

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

	## Model Description

	This model is a full-parameter fine-tuned version of
	[Codemaster67/Olmo-7b-spe](https://huggingface.co/Codemaster67/Olmo-7b-spe) trained on chemistry
	SMILES strings from the
	[Codemaster67/Causal_lm_chemistry_1M_rows](https://huggingface.co/datasets/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

	```python
	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.