Instructions to use jasoncarreira/hrm-text-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jasoncarreira/hrm-text-code with Transformers:

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

pipe = pipeline("text-generation", model="jasoncarreira/hrm-text-code")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("jasoncarreira/hrm-text-code")
model = AutoModelForMultimodalLM.from_pretrained("jasoncarreira/hrm-text-code")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jasoncarreira/hrm-text-code with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/jasoncarreira/hrm-text-code

SGLang

How to use jasoncarreira/hrm-text-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 "jasoncarreira/hrm-text-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": "jasoncarreira/hrm-text-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 "jasoncarreira/hrm-text-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": "jasoncarreira/hrm-text-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jasoncarreira/hrm-text-code with Docker Model Runner:
```
docker model run hf.co/jasoncarreira/hrm-text-code
```

hrm-text-code / README.md

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metadata

license: cc-by-nc-4.0
base_model: sapientinc/HRM-Text-1B
library_name: transformers
pipeline_tag: text-generation
language:
  - en
tags:
  - code
  - code-generation
  - hrm
  - hierarchical-reasoning
  - prefix-lm

HRM-Text-1B-code — a code expert (SFT)

Full-parameter SFT of sapientinc/HRM-Text-1B for Python code generation, trained in the model's synth,cot (reasoning) condition lane. It takes a base that essentially couldn't code (HumanEval 1.2%) and teaches it to code from just ~25k instruction→code SFT examples.

Built as the second expert in a skill-composition experiment (can an HRM tool expert + code expert merge into one model?). Full writeup + code: https://github.com/jasoncarreira/hrm-text-agent. Companions: hrm-text-agent (tools), hrm-text-agent-v2 (tools, scaled).

Scores (pass@1)

Bench	Base	This model
HumanEval	1.2% (2/164)	11.0% (18/164)
MBPP	2.3% (6/257)	16.7% (43/257)

Honest positioning: as a standalone code model this is entry-level — roughly StarCoderBase-1B tier (15% HE), and well below purpose-built small code models (DeepSeek-Coder-1.3B ~35%, Qwen2.5-Coder-1.5B ~40%+, Phi-1 ~50%). But those were pretrained on hundreds of billions of code tokens; this learned code from **25k SFT examples on a non-code reasoning base**, so the result is about sample efficiency, not absolute code SOTA — and plausibly the recurrent reasoning base helps with code's structured nature. (pass@1 measured with the repo's eval_code.py instruct harness, which can slightly under-measure vs a model's native eval.)

Training

full-parameter SFT (sapientinc cfg_sft recipe: lr 3e-5, cosine to 10%, AdamW(0.9, 0.95) wd 0.1, 3 epochs, max_len 2048, bf16)
synth,cot condition (<|quad_end|><|object_ref_end|>) — deliberately a different lane than the tool expert's direct, for the composition experiment
data: ~25k instruction→code examples from CodeFeedback-Filtered-Instruction
- CodeAlpaca-20k, length-filtered to fit 2048

Usage

HRM-Text is a PrefixLM with a conditioning scheme — generate in the synth,cot lane with token_type_ids=1 over the prompt. Use the repo harness rather than a bare .generate():

python eval_code.py --bench humaneval --model jasoncarreira/hrm-text-code

Note on composition

The merge experiment found this code expert and the tool expert do not compose in merged weights — a hard tool-XOR-code trade at every coefficient (tools work only at full tool-weight, where code dies; weaken tools at all and they collapse while code recovers). So for a multi-skill HRM agent the path is model-routing between separate experts, not weight-merging. Details in the repo README.

License & lineage

Base is Apache-2.0; the training data (CodeAlpaca / CodeFeedback lineage) is best treated as non-commercial / research. Verify source licenses for your use case.

🤖 Built with Claude Code.