Instructions to use josephmayo/ZAYA1-8B-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use josephmayo/ZAYA1-8B-Coder with Transformers:

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

pipe = pipeline("text-generation", model="josephmayo/ZAYA1-8B-Coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("josephmayo/ZAYA1-8B-Coder")
model = AutoModelForCausalLM.from_pretrained("josephmayo/ZAYA1-8B-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/ZAYA1-8B-Coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "josephmayo/ZAYA1-8B-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/ZAYA1-8B-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/josephmayo/ZAYA1-8B-Coder

SGLang

How to use josephmayo/ZAYA1-8B-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/ZAYA1-8B-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/ZAYA1-8B-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/ZAYA1-8B-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/ZAYA1-8B-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use josephmayo/ZAYA1-8B-Coder with Docker Model Runner:
```
docker model run hf.co/josephmayo/ZAYA1-8B-Coder
```

ZAYA1-8B-Coder / README.md

josephmayo

Add local evidence artifacts and model-card evidence links

9cf1a6e verified about 1 month ago

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	---
	base_model: Zyphra/ZAYA1-8B
	library_name: transformers
	tags:
	- zaya
	- lora
	- coder
	- merged
	---

	# ZAYA1-8B Coder

	Merged Coder model from `Zyphra/ZAYA1-8B` and `josephmayo/ZAYA1-8B-Coder-LoRA`. This repo contains the adapter merged into the base weights as normal safetensors shards.

	## Evaluation Gate

	The adapter was evaluated against the base model on 50 Python code-generation prompts with a 0-10 heuristic score:

	- Base average: 2.36 / 10
	- LoRA average: 4.76 / 10
	- Absolute score delta: +2.40 / 10
	- Full-scale lift: 24.00%
	- Relative lift over base average: 101.69%
	- Improved prompts: 39 / 50
	- Merge threshold: 20.00%
	- Merge decision: true

	Full-scale lift is the required notebook metric:

	```text
	((lora_avg - base_avg) / 10) * 100
	((4.76 - 2.36) / 10) * 100 = 24.00%
	```

	## Scoring Heuristic

	Each response was scored out of 10:

	- `def` present: 2 points
	- `class` present: 1 point
	- `return` present: 1 point
	- `import` or `from` present: 1 point
	- fenced code block present: 1 point
	- output length greater than 100 characters: 1 point
	- Python AST parse validity: 3 points

	## Architecture Notes

	ZAYA uses a custom `model_type = zaya`; it is not weight-compatible with `LlamaForCausalLM` despite similar naming in some configs. During evaluation and merge, the real ZAYA architecture was loaded using Zyphra's Transformers implementation:

	```bash
	pip install git+https://github.com/Zyphra/transformers.git@zaya1
	```

	The LoRA adapter contains 160 tensors targeting:

	- `self_attn.o_proj`
	- `zaya_block.router.down_proj`

	The merge was performed after the evaluation gate passed, then the merged model was saved to safetensors shards with tokenizer and generation config.

	Evaluation artifacts are included under `eval/`:

	- `eval/eval_summary.json`
	- `eval/score_table.csv`
	- `eval/base_outputs.jsonl`
	- `eval/lora_outputs.jsonl`

	## Included Files

	- `model-00001-of-00005.safetensors` through `model-00005-of-00005.safetensors`
	- `model.safetensors.index.json`
	- `config.json`
	- `generation_config.json`
	- tokenizer files
	- `zaya_patched_config.json`
	- evaluation outputs under `eval/`

	The GGUF quantized release is available at `josephmayo/ZAYA1-8B-Coder-GGUF`.

	<!-- evidence-files:start -->

	## Evidence files

	Run evidence for this release is stored in the repository under `evidence/`:

	- [`evidence/zaya_qlora_eval_result_zaya1-8b-coding-qlora-eval_release_summary.json`](./evidence/zaya_qlora_eval_result_zaya1-8b-coding-qlora-eval_release_summary.json)

	These files are compact local/Kaggle run artifacts used to document training, evaluation, merge, or quantization evidence for this model family.

	<!-- evidence-files:end -->