Image-Text-to-Text
Transformers
Safetensors
qwen3_5
merlina
grimoire
vision-language-model
sft
conversational
Instructions to use nbeerbower/Hemlock-Qwopus3.5-9B-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("nbeerbower/Hemlock-Qwopus3.5-9B-Coder") model = AutoModelForImageTextToText.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
Add model card with training configuration
Browse files
README.md
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| 1 |
+
---
|
| 2 |
+
library_name: transformers
|
| 3 |
+
pipeline_tag: image-text-to-text
|
| 4 |
+
tags:
|
| 5 |
+
- merlina
|
| 6 |
+
- grimoire
|
| 7 |
+
- image-text-to-text
|
| 8 |
+
- vision-language-model
|
| 9 |
+
- sft
|
| 10 |
+
datasets:
|
| 11 |
+
- hemlang/Hemlock2-DPO
|
| 12 |
+
- hemlang/hemlock-formulary-SFT
|
| 13 |
+
- hemlang/hemlock-codex-SFT
|
| 14 |
+
base_model:
|
| 15 |
+
- Jackrong/Qwopus3.5-9B-Coder
|
| 16 |
+
---
|
| 17 |
+
|
| 18 |
+
# Hemlock-Qwopus3.5-9B-Coder
|
| 19 |
+
|
| 20 |
+
## Training Configuration
|
| 21 |
+
|
| 22 |
+
| Parameter | Value |
|
| 23 |
+
|-----------|-------|
|
| 24 |
+
| Training Mode | SFT |
|
| 25 |
+
| Base Model | `Jackrong/Qwopus3.5-9B-Coder` |
|
| 26 |
+
| Learning Rate | 0.0002 |
|
| 27 |
+
| Epochs | 2 |
|
| 28 |
+
| Batch Size | 2 |
|
| 29 |
+
| Gradient Accumulation | 8 |
|
| 30 |
+
| Effective Batch Size | 16 |
|
| 31 |
+
| Max Sequence Length | 4096 |
|
| 32 |
+
| Optimizer | paged_adamw_8bit |
|
| 33 |
+
| LR Scheduler | cosine |
|
| 34 |
+
| Warmup Ratio | 0.05 |
|
| 35 |
+
| Weight Decay | 0.01 |
|
| 36 |
+
| Max Grad Norm | 1.0 |
|
| 37 |
+
| Seed | 42 |
|
| 38 |
+
| LoRA Rank (r) | 256 |
|
| 39 |
+
| LoRA Alpha | 256 |
|
| 40 |
+
| LoRA Dropout | 0.05 |
|
| 41 |
+
| Target Modules | k_proj, o_proj, q_proj, v_proj, down_proj, gate_proj, up_proj |
|
| 42 |
+
| Quantization | 4-bit (NF4) |
|
| 43 |
+
| GPU | NVIDIA RTX A6000 |
|
| 44 |
+
|
| 45 |
+
## Datasets
|
| 46 |
+
|
| 47 |
+
Trained on 3 concatenated datasets:
|
| 48 |
+
|
| 49 |
+
1. [`hemlang/Hemlock2-DPO`](https://huggingface.co/datasets/hemlang/Hemlock2-DPO) (split: `train`)
|
| 50 |
+
2. [`hemlang/hemlock-formulary-SFT`](https://huggingface.co/datasets/hemlang/hemlock-formulary-SFT) (split: `train`)
|
| 51 |
+
3. [`hemlang/hemlock-codex-SFT`](https://huggingface.co/datasets/hemlang/hemlock-codex-SFT) (split: `train`)
|
| 52 |
+
|
| 53 |
+
## Reproduce this training run
|
| 54 |
+
|
| 55 |
+
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.
|
| 56 |
+
|
| 57 |
+
```json
|
| 58 |
+
{
|
| 59 |
+
"_metadata": {
|
| 60 |
+
"name": "Hemlock-Qwopus3.5-9B-Coder",
|
| 61 |
+
"description": "Training configuration shared from a Merlina-trained model.",
|
| 62 |
+
"tags": [],
|
| 63 |
+
"schema": "merlina/training-config",
|
| 64 |
+
"schema_version": 1,
|
| 65 |
+
"merlina_version": "2.0.1"
|
| 66 |
+
},
|
| 67 |
+
"base_model": "Jackrong/Qwopus3.5-9B-Coder",
|
| 68 |
+
"output_name": "Hemlock-Qwopus3.5-9B-Coder",
|
| 69 |
+
"use_lora": true,
|
| 70 |
+
"lora_r": 256,
|
| 71 |
+
"lora_alpha": 256,
|
| 72 |
+
"lora_dropout": 0.05,
|
| 73 |
+
"target_modules": [
|
| 74 |
+
"k_proj",
|
| 75 |
+
"o_proj",
|
| 76 |
+
"q_proj",
|
| 77 |
+
"v_proj",
|
| 78 |
+
"down_proj",
|
| 79 |
+
"gate_proj",
|
| 80 |
+
"up_proj"
|
| 81 |
+
],
|
| 82 |
+
"modules_to_save": [],
|
| 83 |
+
"lora_task_type": "CAUSAL_LM",
|
| 84 |
+
"learning_rate": 0.0002,
|
| 85 |
+
"num_epochs": 2,
|
| 86 |
+
"batch_size": 2,
|
| 87 |
+
"gradient_accumulation_steps": 8,
|
| 88 |
+
"max_length": 4096,
|
| 89 |
+
"max_prompt_length": 1024,
|
| 90 |
+
"model_type": "auto",
|
| 91 |
+
"training_mode": "sft",
|
| 92 |
+
"beta": 0.1,
|
| 93 |
+
"label_smoothing": 0.0,
|
| 94 |
+
"gamma": 0.5,
|
| 95 |
+
"vision_model_id": null,
|
| 96 |
+
"stage": null,
|
| 97 |
+
"unfreeze_vision_top_n": null,
|
| 98 |
+
"image_token_id": null,
|
| 99 |
+
"min_pixels": null,
|
| 100 |
+
"max_pixels": null,
|
| 101 |
+
"image_column": null,
|
| 102 |
+
"caption_column": null,
|
| 103 |
+
"instruction": null,
|
| 104 |
+
"streaming": null,
|
| 105 |
+
"model_name": null,
|
| 106 |
+
"image_resolution": 1024,
|
| 107 |
+
"lora_rank": 32,
|
| 108 |
+
"lora_target_modules": null,
|
| 109 |
+
"lora_use_dora": false,
|
| 110 |
+
"mid_training_samples": true,
|
| 111 |
+
"dataset_jsonl_path": null,
|
| 112 |
+
"dataset_name": null,
|
| 113 |
+
"dataset_split": null,
|
| 114 |
+
"sample_prompts": null,
|
| 115 |
+
"sample_num_steps": null,
|
| 116 |
+
"dataset": {
|
| 117 |
+
"source": {
|
| 118 |
+
"source_type": "huggingface",
|
| 119 |
+
"repo_id": "hemlang/Hemlock2-DPO",
|
| 120 |
+
"split": "train",
|
| 121 |
+
"file_path": null,
|
| 122 |
+
"file_format": null,
|
| 123 |
+
"dataset_id": null,
|
| 124 |
+
"streaming": false,
|
| 125 |
+
"streaming_batch_size": 10000,
|
| 126 |
+
"column_mapping": null
|
| 127 |
+
},
|
| 128 |
+
"additional_sources": [
|
| 129 |
+
{
|
| 130 |
+
"source_type": "huggingface",
|
| 131 |
+
"repo_id": "hemlang/hemlock-formulary-SFT",
|
| 132 |
+
"split": "train",
|
| 133 |
+
"file_path": null,
|
| 134 |
+
"file_format": null,
|
| 135 |
+
"dataset_id": null,
|
| 136 |
+
"streaming": false,
|
| 137 |
+
"streaming_batch_size": 10000,
|
| 138 |
+
"column_mapping": {
|
| 139 |
+
"instruction": "prompt",
|
| 140 |
+
"output": "chosen"
|
| 141 |
+
}
|
| 142 |
+
},
|
| 143 |
+
{
|
| 144 |
+
"source_type": "huggingface",
|
| 145 |
+
"repo_id": "hemlang/hemlock-codex-SFT",
|
| 146 |
+
"split": "train",
|
| 147 |
+
"file_path": null,
|
| 148 |
+
"file_format": null,
|
| 149 |
+
"dataset_id": null,
|
| 150 |
+
"streaming": false,
|
| 151 |
+
"streaming_batch_size": 10000,
|
| 152 |
+
"column_mapping": {
|
| 153 |
+
"instruction": "prompt",
|
| 154 |
+
"output": "chosen"
|
| 155 |
+
}
|
| 156 |
+
}
|
| 157 |
+
],
|
| 158 |
+
"format": {
|
| 159 |
+
"format_type": "tokenizer",
|
| 160 |
+
"custom_templates": null,
|
| 161 |
+
"enable_thinking": true
|
| 162 |
+
},
|
| 163 |
+
"model_name": "Jackrong/Qwopus3.5-9B-Coder",
|
| 164 |
+
"column_mapping": {
|
| 165 |
+
"prompt": "prompt",
|
| 166 |
+
"chosen": "chosen",
|
| 167 |
+
"rejected": "rejected"
|
| 168 |
+
},
|
| 169 |
+
"convert_messages_format": true,
|
| 170 |
+
"deduplicate": false,
|
| 171 |
+
"dedupe_strategy": "prompt_chosen",
|
| 172 |
+
"test_size": 0.01,
|
| 173 |
+
"max_samples": null,
|
| 174 |
+
"system_prompt": null,
|
| 175 |
+
"system_prompt_mode": "fill_empty",
|
| 176 |
+
"training_mode": "sft"
|
| 177 |
+
},
|
| 178 |
+
"seed": 42,
|
| 179 |
+
"max_grad_norm": 1.0,
|
| 180 |
+
"warmup_ratio": 0.05,
|
| 181 |
+
"eval_steps": 0.2,
|
| 182 |
+
"use_4bit": true,
|
| 183 |
+
"use_wandb": true,
|
| 184 |
+
"push_to_hub": true,
|
| 185 |
+
"merge_lora_before_upload": true,
|
| 186 |
+
"hf_hub_private": true,
|
| 187 |
+
"export_gguf": false,
|
| 188 |
+
"gguf_quant_types": [
|
| 189 |
+
"Q4_K_M"
|
| 190 |
+
],
|
| 191 |
+
"keep_gguf_fp16": false,
|
| 192 |
+
"shuffle_dataset": true,
|
| 193 |
+
"weight_decay": 0.01,
|
| 194 |
+
"lr_scheduler_type": "cosine",
|
| 195 |
+
"gradient_checkpointing": true,
|
| 196 |
+
"logging_steps": 1,
|
| 197 |
+
"optimizer_type": "paged_adamw_8bit",
|
| 198 |
+
"adam_beta1": 0.9,
|
| 199 |
+
"adam_beta2": 0.999,
|
| 200 |
+
"adam_epsilon": 1e-08,
|
| 201 |
+
"adafactor_relative_step": false,
|
| 202 |
+
"adafactor_scale_parameter": false,
|
| 203 |
+
"adafactor_warmup_init": false,
|
| 204 |
+
"adafactor_decay_rate": -0.8,
|
| 205 |
+
"adafactor_beta1": null,
|
| 206 |
+
"adafactor_clip_threshold": 1.0,
|
| 207 |
+
"attn_implementation": "sdpa",
|
| 208 |
+
"use_liger": true,
|
| 209 |
+
"torch_compile": false,
|
| 210 |
+
"neftune_alpha": null,
|
| 211 |
+
"eval_on_start": false,
|
| 212 |
+
"gpu_ids": null,
|
| 213 |
+
"multi_gpu_strategy": "auto",
|
| 214 |
+
"wandb_project": null,
|
| 215 |
+
"wandb_run_name": null,
|
| 216 |
+
"wandb_tags": null,
|
| 217 |
+
"wandb_notes": null
|
| 218 |
+
}
|
| 219 |
+
```
|
| 220 |
+
|
| 221 |
+
---
|
| 222 |
+
|
| 223 |
+

|
| 224 |
+
|
| 225 |
+
[Merlina on GitHub](https://github.com/Schneewolf-Labs/Merlina)
|