Instructions to use nbeerbower/A2-Hemlock-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nbeerbower/A2-Hemlock-Coder with Transformers:

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

pipe = pipeline("text-generation", model="nbeerbower/A2-Hemlock-Coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nbeerbower/A2-Hemlock-Coder")
model = AutoModelForCausalLM.from_pretrained("nbeerbower/A2-Hemlock-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

vLLM

How to use nbeerbower/A2-Hemlock-Coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nbeerbower/A2-Hemlock-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": "nbeerbower/A2-Hemlock-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nbeerbower/A2-Hemlock-Coder

SGLang

How to use nbeerbower/A2-Hemlock-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 "nbeerbower/A2-Hemlock-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": "nbeerbower/A2-Hemlock-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 "nbeerbower/A2-Hemlock-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": "nbeerbower/A2-Hemlock-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nbeerbower/A2-Hemlock-Coder with Docker Model Runner:
```
docker model run hf.co/nbeerbower/A2-Hemlock-Coder
```

A2-Hemlock-Coder

File size: 1,486 Bytes

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---
library_name: transformers
pipeline_tag: text-generation
tags:
- merlina
- grimoire
- text-generation
- sft
datasets:
- hemlang/Hemlock2-DPO
- hemlang/hemlock-formulary-SFT
- hemlang/hemlock-codex-SFT
base_model:
- nbeerbower/A2-Coder
---

# A2-Hemlock-Coder

## Training Configuration

| Parameter | Value |
|-----------|-------|
| Training Mode | SFT |
| Base Model | `nbeerbower/A2-Coder` |
| Learning Rate | 0.0001 |
| Epochs | 2 |
| Batch Size | 1 |
| Gradient Accumulation | 16 |
| Effective Batch Size | 16 |
| Max Sequence Length | 2048 |
| Optimizer | paged_adamw_8bit |
| LR Scheduler | cosine |
| Warmup Ratio | 0.05 |
| Weight Decay | 0.01 |
| Max Grad Norm | 0.5 |
| Seed | 42 |
| LoRA Rank (r) | 128 |
| LoRA Alpha | 128 |
| LoRA Dropout | 0.05 |
| Target Modules | k_proj, o_proj, q_proj, v_proj, down_proj, gate_proj, up_proj |
| Quantization | 4-bit (NF4) |
| GPU | NVIDIA RTX A6000 |

## Datasets

Trained on 3 concatenated datasets:

1. [`hemlang/Hemlock2-DPO`](https://huggingface.co/datasets/hemlang/Hemlock2-DPO) (split: `train`)
2. [`hemlang/hemlock-formulary-SFT`](https://huggingface.co/datasets/hemlang/hemlock-formulary-SFT) (split: `train`)
3. [`hemlang/hemlock-codex-SFT`](https://huggingface.co/datasets/hemlang/hemlock-codex-SFT) (split: `train`)

---

![Trained with Merlina](https://raw.githubusercontent.com/Schneewolf-Labs/Merlina/refs/heads/main/frontend/madewithmerlina_smol.png)

[Merlina on GitHub](https://github.com/Schneewolf-Labs/Merlina)