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, AutoModelForCausalLM

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

josephmayo

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	---
	base_model: sapientinc/HRM-Text-1B
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- transformers
	- code
	- python
	- humaneval
	- mbpp
	---

	# HRM-Text-1B-sft-code

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

	[`josephmayo/HRM-Text-1B-sft-code-LoRA`](https://huggingface.co/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

	```python
	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`