Instructions to use josephmayo/HRM-Text-1B-sft-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use josephmayo/HRM-Text-1B-sft-code with Transformers:

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

pipe = pipeline("text-generation", model="josephmayo/HRM-Text-1B-sft-code")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("josephmayo/HRM-Text-1B-sft-code")
model = AutoModelForMultimodalLM.from_pretrained("josephmayo/HRM-Text-1B-sft-code")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use josephmayo/HRM-Text-1B-sft-code with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "josephmayo/HRM-Text-1B-sft-code"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "josephmayo/HRM-Text-1B-sft-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/josephmayo/HRM-Text-1B-sft-code

SGLang

How to use josephmayo/HRM-Text-1B-sft-code 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 "josephmayo/HRM-Text-1B-sft-code" \
    --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": "josephmayo/HRM-Text-1B-sft-code",
		"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 "josephmayo/HRM-Text-1B-sft-code" \
        --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": "josephmayo/HRM-Text-1B-sft-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use josephmayo/HRM-Text-1B-sft-code with Docker Model Runner:
```
docker model run hf.co/josephmayo/HRM-Text-1B-sft-code
```

HRM-Text-1B-sft-code

Merged code post-training release from sapientinc/HRM-Text-1B plus:

josephmayo/HRM-Text-1B-sft-code-LoRA

sapientinc/HRM-Text-1B is a pretrained-only HRM text model. This merged release packages the code post-trained LoRA into the base weights for direct use.

Training Summary

Base model: sapientinc/HRM-Text-1B
Method: supervised LoRA post-training, then merged into base weights
Training rows: 384
Max steps: 120
LoRA rank: 64
Learning rate: 8e-6
Final train loss: 0.3275703112284342

Validation

Local code validation:

Base model score: 5/100
Merged model score: 24/100
Absolute improvement: +19/100
Relative improvement: 4.8x over base
HumanEval slice: 14/50
MBPP slice: 10/50

The score above is the local validation result used for this release.

Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "josephmayo/HRM-Text-1B-sft-code"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
model.eval()

Notes

This is the merged release of the LoRA.
Adapter repo: josephmayo/HRM-Text-1B-sft-code-LoRA

Downloads last month: 99

Safetensors

Model size

1B params

Tensor type

F16

Model tree for josephmayo/HRM-Text-1B-sft-code

Base model

sapientinc/HRM-Text-1B

Finetuned

(10)

this model