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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("josephmayo/ZAYA1-8B-Coder")
model = AutoModelForCausalLM.from_pretrained("josephmayo/ZAYA1-8B-Coder", device_map="auto")
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=256)
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
```

josephmayo commited on May 27

Commit

7ca7233

verified ·

1 Parent(s): 2f128c2

Update model card with eval summary

Browse files

Files changed (1) hide show

README.md +29 -1

README.md CHANGED Viewed

@@ -1,5 +1,33 @@
 # ZAYA1-8B Coding Standalone
 Merged standalone model from `Zyphra/ZAYA1-8B` and `josephmayo/ZAYA1-8B-Coding-LoRA`.
-Evaluation gate: 24.00% full-scale improvement (threshold 20.00%).

+---
+base_model: Zyphra/ZAYA1-8B
+library_name: transformers
+tags:
+- zaya
+- lora
+- coding
+- merged
+---
 # ZAYA1-8B Coding Standalone
 Merged standalone model from `Zyphra/ZAYA1-8B` and `josephmayo/ZAYA1-8B-Coding-LoRA`.
+## Evaluation Gate
+The adapter was evaluated against the base model on 50 Python coding prompts with a 0-10 heuristic score:
+- Base average: 2.36 / 10
+- LoRA average: 4.76 / 10
+- Improved prompts: 39 / 50
+- Full-scale improvement: 24.00%
+- Merge threshold: 20.00%
+- Merge decision: true
+Evaluation artifacts are included under `eval/`:
+- `eval/eval_summary.json`
+- `eval/score_table.csv`
+- `eval/base_outputs.jsonl`
+- `eval/lora_outputs.jsonl`
+Note: ZAYA uses a custom `model_type = zaya`; loading the original architecture requires Zyphra's Transformers implementation. A patched `zaya_patched_config.json` is also included for the requested compatibility artifact.