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

Libraries

How to use InterstellarCG/HRM-Text-1B-Code-FT with Transformers:

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

pipe = pipeline("text-generation", model="InterstellarCG/HRM-Text-1B-Code-FT")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("InterstellarCG/HRM-Text-1B-Code-FT")
model = AutoModelForCausalLM.from_pretrained("InterstellarCG/HRM-Text-1B-Code-FT")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use InterstellarCG/HRM-Text-1B-Code-FT with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/InterstellarCG/HRM-Text-1B-Code-FT

SGLang

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

Docker Model Runner
How to use InterstellarCG/HRM-Text-1B-Code-FT with Docker Model Runner:
```
docker model run hf.co/InterstellarCG/HRM-Text-1B-Code-FT
```

HRM-Text-1B-Code-FT / README.md

InterstellarCG

Upload HRM-Text-1B Code Fine-tuned epoch 3

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	---
	license: mit
	language:
	- en
	library_name: transformers
	tags:
	- code
	- text-generation
	- hrm-text
	- fine-tuned
	base_model: sapientai/hrm-text-1b
	---

	# HRM-Text-1B Code Fine-tuned

	Fine-tuned from HRM-Text-1B on combined code dataset (192M tokens).

	## Training Details
	- Base: HRM-Text-1B (stacked from HTML/CSS 100k checkpoint)
	- Dataset: Combined code (Python, JavaScript, TypeScript, SQL, HTML/CSS)
	- Tokens: 192M
	- Epochs: 3
	- Learning rate: 1e-5

	## Capabilities
	- Python code generation
	- JavaScript functions
	- SQL queries
	- General QA (improved over base)

	## Limitations
	- Weak at React/TSX syntax
	- HTML/CSS output can be malformed
	- TypeScript interfaces not well-formed

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained("InterstellarCG/HRM-Text-1B-Code-FT")
	tokenizer = AutoTokenizer.from_pretrained("InterstellarCG/HRM-Text-1B-Code-FT")
	```

	## Evaluation

	\| Task \| Base \| Fine-tuned \|
	\|------\|------\|------------\|
	\| Python (is_prime) \| Garbage \| Correct \|
	\| JS (reverse array) \| Garbage \| Correct \|
	\| SQL (join query) \| Garbage \| Correct \|
	\| QA (Paris capital) \| Garbage \| Correct \|