Instructions to use nbeerbower/Hemlock-Qwopus3.5-9B-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nbeerbower/Hemlock-Qwopus3.5-9B-Coder with Transformers:

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

pipe = pipeline("image-text-to-text", model="nbeerbower/Hemlock-Qwopus3.5-9B-Coder")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("nbeerbower/Hemlock-Qwopus3.5-9B-Coder")
model = AutoModelForMultimodalLM.from_pretrained("nbeerbower/Hemlock-Qwopus3.5-9B-Coder")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nbeerbower/Hemlock-Qwopus3.5-9B-Coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nbeerbower/Hemlock-Qwopus3.5-9B-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/Hemlock-Qwopus3.5-9B-Coder",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/nbeerbower/Hemlock-Qwopus3.5-9B-Coder

SGLang

How to use nbeerbower/Hemlock-Qwopus3.5-9B-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/Hemlock-Qwopus3.5-9B-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/Hemlock-Qwopus3.5-9B-Coder",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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/Hemlock-Qwopus3.5-9B-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/Hemlock-Qwopus3.5-9B-Coder",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use nbeerbower/Hemlock-Qwopus3.5-9B-Coder with Docker Model Runner:
```
docker model run hf.co/nbeerbower/Hemlock-Qwopus3.5-9B-Coder
```

Hemlock-Qwopus3.5-9B-Coder / README.md

nbeerbower

Add model card with training configuration

60de5f3 verified 8 days ago

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	---
	library_name: transformers
	pipeline_tag: image-text-to-text
	tags:
	- merlina
	- grimoire
	- image-text-to-text
	- vision-language-model
	- sft
	datasets:
	- hemlang/Hemlock2-DPO
	- hemlang/hemlock-formulary-SFT
	- hemlang/hemlock-codex-SFT
	base_model:
	- Jackrong/Qwopus3.5-9B-Coder
	---

	# Hemlock-Qwopus3.5-9B-Coder

	## Training Configuration

	\| Parameter \| Value \|
	\|-----------\|-------\|
	\| Training Mode \| SFT \|
	\| Base Model \| `Jackrong/Qwopus3.5-9B-Coder` \|
	\| Learning Rate \| 0.0002 \|
	\| Epochs \| 2 \|
	\| Batch Size \| 2 \|
	\| Gradient Accumulation \| 8 \|
	\| Effective Batch Size \| 16 \|
	\| Max Sequence Length \| 4096 \|
	\| Optimizer \| paged_adamw_8bit \|
	\| LR Scheduler \| cosine \|
	\| Warmup Ratio \| 0.05 \|
	\| Weight Decay \| 0.01 \|
	\| Max Grad Norm \| 1.0 \|
	\| Seed \| 42 \|
	\| LoRA Rank (r) \| 256 \|
	\| LoRA Alpha \| 256 \|
	\| 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`)

	## Reproduce this training run

	This model was trained with [Merlina](https://github.com/Schneewolf-Labs/Merlina). Save the JSON below to `data/configs/<name>.json` (or import it via the Load Configuration dialog) to reproduce the exact training setup. Credentials are not included — Merlina will use your own `HF_TOKEN` and `WANDB_API_KEY` from `.env` or the form.

	```json
	{
	"_metadata": {
	"name": "Hemlock-Qwopus3.5-9B-Coder",
	"description": "Training configuration shared from a Merlina-trained model.",
	"tags": [],
	"schema": "merlina/training-config",
	"schema_version": 1,
	"merlina_version": "2.0.1"
	},
	"base_model": "Jackrong/Qwopus3.5-9B-Coder",
	"output_name": "Hemlock-Qwopus3.5-9B-Coder",
	"use_lora": true,
	"lora_r": 256,
	"lora_alpha": 256,
	"lora_dropout": 0.05,
	"target_modules": [
	"k_proj",
	"o_proj",
	"q_proj",
	"v_proj",
	"down_proj",
	"gate_proj",
	"up_proj"
	],
	"modules_to_save": [],
	"lora_task_type": "CAUSAL_LM",
	"learning_rate": 0.0002,
	"num_epochs": 2,
	"batch_size": 2,
	"gradient_accumulation_steps": 8,
	"max_length": 4096,
	"max_prompt_length": 1024,
	"model_type": "auto",
	"training_mode": "sft",
	"beta": 0.1,
	"label_smoothing": 0.0,
	"gamma": 0.5,
	"vision_model_id": null,
	"stage": null,
	"unfreeze_vision_top_n": null,
	"image_token_id": null,
	"min_pixels": null,
	"max_pixels": null,
	"image_column": null,
	"caption_column": null,
	"instruction": null,
	"streaming": null,
	"model_name": null,
	"image_resolution": 1024,
	"lora_rank": 32,
	"lora_target_modules": null,
	"lora_use_dora": false,
	"mid_training_samples": true,
	"dataset_jsonl_path": null,
	"dataset_name": null,
	"dataset_split": null,
	"sample_prompts": null,
	"sample_num_steps": null,
	"dataset": {
	"source": {
	"source_type": "huggingface",
	"repo_id": "hemlang/Hemlock2-DPO",
	"split": "train",
	"file_path": null,
	"file_format": null,
	"dataset_id": null,
	"streaming": false,
	"streaming_batch_size": 10000,
	"column_mapping": null
	},
	"additional_sources": [
	{
	"source_type": "huggingface",
	"repo_id": "hemlang/hemlock-formulary-SFT",
	"split": "train",
	"file_path": null,
	"file_format": null,
	"dataset_id": null,
	"streaming": false,
	"streaming_batch_size": 10000,
	"column_mapping": {
	"instruction": "prompt",
	"output": "chosen"
	}
	},
	{
	"source_type": "huggingface",
	"repo_id": "hemlang/hemlock-codex-SFT",
	"split": "train",
	"file_path": null,
	"file_format": null,
	"dataset_id": null,
	"streaming": false,
	"streaming_batch_size": 10000,
	"column_mapping": {
	"instruction": "prompt",
	"output": "chosen"
	}
	}
	],
	"format": {
	"format_type": "tokenizer",
	"custom_templates": null,
	"enable_thinking": true
	},
	"model_name": "Jackrong/Qwopus3.5-9B-Coder",
	"column_mapping": {
	"prompt": "prompt",
	"chosen": "chosen",
	"rejected": "rejected"
	},
	"convert_messages_format": true,
	"deduplicate": false,
	"dedupe_strategy": "prompt_chosen",
	"test_size": 0.01,
	"max_samples": null,
	"system_prompt": null,
	"system_prompt_mode": "fill_empty",
	"training_mode": "sft"
	},
	"seed": 42,
	"max_grad_norm": 1.0,
	"warmup_ratio": 0.05,
	"eval_steps": 0.2,
	"use_4bit": true,
	"use_wandb": true,
	"push_to_hub": true,
	"merge_lora_before_upload": true,
	"hf_hub_private": true,
	"export_gguf": false,
	"gguf_quant_types": [
	"Q4_K_M"
	],
	"keep_gguf_fp16": false,
	"shuffle_dataset": true,
	"weight_decay": 0.01,
	"lr_scheduler_type": "cosine",
	"gradient_checkpointing": true,
	"logging_steps": 1,
	"optimizer_type": "paged_adamw_8bit",
	"adam_beta1": 0.9,
	"adam_beta2": 0.999,
	"adam_epsilon": 1e-08,
	"adafactor_relative_step": false,
	"adafactor_scale_parameter": false,
	"adafactor_warmup_init": false,
	"adafactor_decay_rate": -0.8,
	"adafactor_beta1": null,
	"adafactor_clip_threshold": 1.0,
	"attn_implementation": "sdpa",
	"use_liger": true,
	"torch_compile": false,
	"neftune_alpha": null,
	"eval_on_start": false,
	"gpu_ids": null,
	"multi_gpu_strategy": "auto",
	"wandb_project": null,
	"wandb_run_name": null,
	"wandb_tags": null,
	"wandb_notes": null
	}
	```

	---

	![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)