hemlang/hemlock-codex-SFT
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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]:]))How to use nbeerbower/Hemlock-Qwopus3.5-9B-Coder with vLLM:
# 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"
}
}
]
}
]
}'docker model run hf.co/nbeerbower/Hemlock-Qwopus3.5-9B-Coder
How to use nbeerbower/Hemlock-Qwopus3.5-9B-Coder with SGLang:
# 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"
}
}
]
}
]
}'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"
}
}
]
}
]
}'How to use nbeerbower/Hemlock-Qwopus3.5-9B-Coder with Docker Model Runner:
docker model run hf.co/nbeerbower/Hemlock-Qwopus3.5-9B-Coder
| 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 |
Trained on 3 concatenated datasets:
hemlang/Hemlock2-DPO (split: train)hemlang/hemlock-formulary-SFT (split: train)hemlang/hemlock-codex-SFT (split: train)This model was trained with 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.
{
"_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
}
Base model
Qwen/Qwen3.5-9B-Base