Instructions to use josephmayo/Holo-3.1-4B-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use josephmayo/Holo-3.1-4B-Coder with Transformers:

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

pipe = pipeline("text-generation", model="josephmayo/Holo-3.1-4B-Coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("josephmayo/Holo-3.1-4B-Coder")
model = AutoModelForMultimodalLM.from_pretrained("josephmayo/Holo-3.1-4B-Coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use josephmayo/Holo-3.1-4B-Coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "josephmayo/Holo-3.1-4B-Coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "josephmayo/Holo-3.1-4B-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/josephmayo/Holo-3.1-4B-Coder

SGLang

How to use josephmayo/Holo-3.1-4B-Coder 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/Holo-3.1-4B-Coder" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "josephmayo/Holo-3.1-4B-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/Holo-3.1-4B-Coder" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "josephmayo/Holo-3.1-4B-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use josephmayo/Holo-3.1-4B-Coder with Docker Model Runner:
```
docker model run hf.co/josephmayo/Holo-3.1-4B-Coder
```

josephmayo commited on 5 days ago

Commit

a6515dc

verified ·

1 Parent(s): cea398a

Update model card

Browse files

Files changed (1) hide show

README.md +58 -6

README.md CHANGED Viewed

@@ -1,6 +1,58 @@
-# Holo-3.1-4B-Coding-Repair38
-Merged coding SFT model from `Hcompany/Holo-3.1-4B` and adapter `josephmayo/Holo-3.1-4B-Coding-Repair38-LoRA`.
-This release is published after an explicit override of the original gate. The held-out
-HumanEval+MBPP probe improved from 24/80 to 31/80, a 29.17% relative lift.

+---
+base_model: Hcompany/Holo-3.1-4B
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- coding
+- merged
+- safetensors
+- python
+---
+# Holo-3.1-4B Coding Merged Model
+## Overview
+This repository contains a merged Transformers checkpoint produced from `Hcompany/Holo-3.1-4B` and the companion coding LoRA adapter. It is intended for users who prefer loading a standard merged model rather than applying a PEFT adapter at runtime.
+## What Is Included
+- Merged model weights in sharded `safetensors` format.
+- Model configuration and generation configuration.
+- Tokenizer and chat template files.
+- A model card summarizing the measured coding adaptation result.
+## Training And Evaluation Summary
+The underlying adapter was trained with supervised fine-tuning on curated coding instruction data. Evaluation used an 80-task held-out greedy decoding probe drawn from HumanEval-style and MBPP-style tasks.
+Measured result on the held-out probe:
+- Base model: 24 / 80 tasks passed.
+- Adapted model: 31 / 80 tasks passed.
+- Relative lift over the measured base result: 29.17%.
+The merged model should match the adapter-applied behavior, subject to normal numerical and runtime differences.
+## Intended Use
+Use this checkpoint for coding assistance experiments, Python function generation, lightweight algorithmic problem solving, and local inference workflows that expect standard Transformers model files.
+## Known Limitations
+- The evaluation probe is small and does not cover all programming languages or repository-scale workflows.
+- The model can produce incorrect code, incomplete reasoning, or solutions that fail edge cases.
+- Generated code should be reviewed, tested, and sandboxed where appropriate.
+- The checkpoint inherits limitations and licensing terms from the base model and adaptation data sources.
+## File List
+- `model-00001-of-00009.safetensors` through `model-00009-of-00009.safetensors`: merged model shards.
+- `model.safetensors.index.json`: shard index.
+- `config.json`, `generation_config.json`: model configuration files.
+- `tokenizer.json`, `tokenizer_config.json`, `chat_template.jinja`: tokenizer/chat assets.
+- `README.md`: this model card.
+## Reproducibility And Provenance
+The model was produced by merging a PEFT LoRA coding adapter into `Hcompany/Holo-3.1-4B` and saving the result as sharded `safetensors`. Companion evaluation and training provenance artifacts are available in the LoRA repository.